Dr. Jagannath E . Nalavade (original) (raw)

Papers by Dr. Jagannath E . Nalavade

2021 International Conference on Computer Communication and Informatics (ICCCI), 2021

Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay... more Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay safe don’t go outside, keep social distancing. Means we want to break chain of corona virus affected people. Till date there is no any automation tool is designed to detect Covid-19 patients. Such type of automation tool is required to detect patient at earlier stage(during first week of infection). If we detect Covid-19 patients in earlier stage and take required actions, we can save all patients.In this proposal we want to design automated digital screening tool to identify the people in the first week of the pandemic. Based on machine learning techniques, system will be trained as per the day to day symptoms. ASHA workers from Rural Health Centre can collect the all the peoples information related to Covid-19. By using Classification algorithm system will classify the people into different categories like healthy people (no any symptoms), first day of covid-19, second day covid-19, upto fourteenth day of covid-19. Data set required for training the model will be created by studying covid-19 patients per day history. Like first day he had aches and pains, nasal congestion, runny nose, sore throat or diarrhea etc., related any symptoms. Similarly, we will prepare dataset of 5000 patients. Using this dataset model will be trained. After training the model whenever such type of pandemic occurs we will detect the infected people in early stage.We will reach to each of the citizen of India through social workers, ASHA worker, Rural Health care staff. Collect information of each person by using attributes which is decided for dataset. We will provide collected dataset (each citizen information) to our designed system. Designed system will classify according to classes mentioned above. If person don’t have any symptoms related to covid-19 means he is healthy. If he had any symptoms according to day we have classified then such type of people immediately informed and according to requirement they will be kept in isolation, quarantine or hospitalized immediately. If we able to identify during the first week of pandemic then we can break chain at early stage and we can avoid spreading of coronavirus to large number of people. If we use such type of system then no need of lockdown for long period is required. Time required only for collecting the information, identifying the infected people and take appropriate action as mentioned above. Maximum 1 week of lockdown will be sufficient and we can stop chain. No any major loss in economy. In future this type of model can be used for any type of pandemic. We will detect infected people at early stage and we can stop chain for any type of pandemic. Here we are designing this type of tool using machine learning, data mining and IoT concepts. Till date nobody has been designed such type of tool to detect covid-19 within less time.Till date nobody has been designed such type of tool to detect covid-19 within less time.

2021 International Conference on Computer Communication and Informatics (ICCCI), 2021

Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay... more Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay safe don’t go outside, keep social distancing. Means we want to break chain of corona virus affected people. Till date there is no any automation tool is designed to detect Covid-19 patients. Such type of automation tool is required to detect patient at earlier stage(during first week of infection). If we detect Covid-19 patients in earlier stage and take required actions, we can save all patients.In this proposal we want to design automated digital screening tool to identify the people in the first week of the pandemic. Based on machine learning techniques, system will be trained as per the day to day symptoms. ASHA workers from Rural Health Centre can collect the all the peoples information related to Covid-19. By using Classification algorithm system will classify the people into different categories like healthy people (no any symptoms), first day of covid-19, second day covid-19, upto fourteenth day of covid-19. Data set required for training the model will be created by studying covid-19 patients per day history. Like first day he had aches and pains, nasal congestion, runny nose, sore throat or diarrhea etc., related any symptoms. Similarly, we will prepare dataset of 5000 patients. Using this dataset model will be trained. After training the model whenever such type of pandemic occurs we will detect the infected people in early stage.We will reach to each of the citizen of India through social workers, ASHA worker, Rural Health care staff. Collect information of each person by using attributes which is decided for dataset. We will provide collected dataset (each citizen information) to our designed system. Designed system will classify according to classes mentioned above. If person don’t have any symptoms related to covid-19 means he is healthy. If he had any symptoms according to day we have classified then such type of people immediately informed and according to requirement they will be kept in isolation, quarantine or hospitalized immediately. If we able to identify during the first week of pandemic then we can break chain at early stage and we can avoid spreading of coronavirus to large number of people. If we use such type of system then no need of lockdown for long period is required. Time required only for collecting the information, identifying the infected people and take appropriate action as mentioned above. Maximum 1 week of lockdown will be sufficient and we can stop chain. No any major loss in economy. In future this type of model can be used for any type of pandemic. We will detect infected people at early stage and we can stop chain for any type of pandemic. Here we are designing this type of tool using machine learning, data mining and IoT concepts. Till date nobody has been designed such type of tool to detect covid-19 within less time.Till date nobody has been designed such type of tool to detect covid-19 within less time.

Journal of Central South University, 2017

The rapid developments in the fields of telecommunication, sensor data, financial applications, a... more The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Journal of King Saud University - Computer and Information Sciences, 2016

Data stream classification plays a vital role in data mining techniques which extracts the most i... more Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm.

International Journal of Knowledge-based and Intelligent Engineering Systems, 2016

Due to the continuous growth of recent applications such as, telecommunication, sensor data, fina... more Due to the continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task in data mining. Among the various tasks involved in data mining, the classification of data streams poses various challenging issues as compared to popular algorithms of data classification. Since the classification algorithm performs endlessly, it must be able to adapt the classification model to handle the change of concept or boundaries between classes. In order to handle these issues, we have developed a new fuzzy system called, HRFuzzy for classification of evolving data streams. Here, rough set theory and holoentropy function are utilized to construct the dynamic classification model. In the fuzzy system, the rules are generated using k-means clustering and membership functions are dynamically updated using holoentropy function. The experimentation of the proposed HRFuzzy is performed using two different databases such as, skin segmentation dataset and localization data. The performance is compared with the adaptive k-NN classifier in terms of accuracy and time. From the outcome, we proved that the proposed HRFuzzy outperformed in both the metrics by giving the maximum performance.

Stream data mining is emerging field in the data mining. Stream data generates from many applicat... more Stream data mining is emerging field in the data mining. Stream data generates from many applications such as banking, sensor networks, blogs at twitter. It is conceptually endless sequences of data records. It is often arriving at high rates. Analyzing or mining data streams raises several new issues compared to standard data mining algorithms. Standard data mining algorithms assume that records can be examined multiple times. Data stream mining algorithms, on the other hand, are more challenging to design since they must be able to extract all necessary information from records with only one pass over the data. Data stream mining algorithms must be online. Arrival rates for records are high, so the practical complexity of processing must also be low. Since the classification algorithm executes endlessly, it must be able to adapt the classification model to changes in the data stream, in particular to changes in the boundaries between classes (“concept drift”). To maximize usefulne...

THRFuzzy: Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams, 2017

The rapid developments in the fields of telecommunication, sensor data, financial applications, a... more The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams,
and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis
process consists of different tasks, among which the data stream classification approaches face more challenges than the other
commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the
classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy
classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function
helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for
the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed
THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the
evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental
results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal
time than the existing classifiers.

Data stream classification plays a vital role in data mining techniques which extracts the most i... more Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm.

The rapid developments in the fields of telecommunication, sensor data, financial applications, a... more The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Due to the continuous growth of recent applications such as, telecommunication, sensor data, fina... more Due to the continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task in data mining. Among the various tasks involved in data mining, the classification of data streams poses various challenging issues as compared to popular algorithms of data classification. Since the classification algorithm performs endlessly, it must be able to adapt the classification model to handle the change of concept or boundaries between classes. In order to handle these issues, we have developed a new fuzzy system called, HRFuzzy for classification of evolving data streams. Here, rough set theory and holoentropy function are utilized to construct the dynamic classification model. In the fuzzy system, the rules are generated using k-means clustering and membership functions are dynamically updated using holoentropy function. The experimentation of the proposed HRFuzzy is performed using two different databases such as, skin segmentation dataset and localization data. The performance is compared with the adaptive k-NN classifier in terms of accuracy and time. From the outcome, we proved that the proposed HRFuzzy outperformed in both the metrics by giving the maximum performance.

2021 International Conference on Computer Communication and Informatics (ICCCI), 2021

Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay... more Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay safe don’t go outside, keep social distancing. Means we want to break chain of corona virus affected people. Till date there is no any automation tool is designed to detect Covid-19 patients. Such type of automation tool is required to detect patient at earlier stage(during first week of infection). If we detect Covid-19 patients in earlier stage and take required actions, we can save all patients.In this proposal we want to design automated digital screening tool to identify the people in the first week of the pandemic. Based on machine learning techniques, system will be trained as per the day to day symptoms. ASHA workers from Rural Health Centre can collect the all the peoples information related to Covid-19. By using Classification algorithm system will classify the people into different categories like healthy people (no any symptoms), first day of covid-19, second day covid-19, upto fourteenth day of covid-19. Data set required for training the model will be created by studying covid-19 patients per day history. Like first day he had aches and pains, nasal congestion, runny nose, sore throat or diarrhea etc., related any symptoms. Similarly, we will prepare dataset of 5000 patients. Using this dataset model will be trained. After training the model whenever such type of pandemic occurs we will detect the infected people in early stage.We will reach to each of the citizen of India through social workers, ASHA worker, Rural Health care staff. Collect information of each person by using attributes which is decided for dataset. We will provide collected dataset (each citizen information) to our designed system. Designed system will classify according to classes mentioned above. If person don’t have any symptoms related to covid-19 means he is healthy. If he had any symptoms according to day we have classified then such type of people immediately informed and according to requirement they will be kept in isolation, quarantine or hospitalized immediately. If we able to identify during the first week of pandemic then we can break chain at early stage and we can avoid spreading of coronavirus to large number of people. If we use such type of system then no need of lockdown for long period is required. Time required only for collecting the information, identifying the infected people and take appropriate action as mentioned above. Maximum 1 week of lockdown will be sufficient and we can stop chain. No any major loss in economy. In future this type of model can be used for any type of pandemic. We will detect infected people at early stage and we can stop chain for any type of pandemic. Here we are designing this type of tool using machine learning, data mining and IoT concepts. Till date nobody has been designed such type of tool to detect covid-19 within less time.Till date nobody has been designed such type of tool to detect covid-19 within less time.

2021 International Conference on Computer Communication and Informatics (ICCCI), 2021

Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay... more Covid-19 pandemic is increasing day by day in world. Government agencies informing people to stay safe don’t go outside, keep social distancing. Means we want to break chain of corona virus affected people. Till date there is no any automation tool is designed to detect Covid-19 patients. Such type of automation tool is required to detect patient at earlier stage(during first week of infection). If we detect Covid-19 patients in earlier stage and take required actions, we can save all patients.In this proposal we want to design automated digital screening tool to identify the people in the first week of the pandemic. Based on machine learning techniques, system will be trained as per the day to day symptoms. ASHA workers from Rural Health Centre can collect the all the peoples information related to Covid-19. By using Classification algorithm system will classify the people into different categories like healthy people (no any symptoms), first day of covid-19, second day covid-19, upto fourteenth day of covid-19. Data set required for training the model will be created by studying covid-19 patients per day history. Like first day he had aches and pains, nasal congestion, runny nose, sore throat or diarrhea etc., related any symptoms. Similarly, we will prepare dataset of 5000 patients. Using this dataset model will be trained. After training the model whenever such type of pandemic occurs we will detect the infected people in early stage.We will reach to each of the citizen of India through social workers, ASHA worker, Rural Health care staff. Collect information of each person by using attributes which is decided for dataset. We will provide collected dataset (each citizen information) to our designed system. Designed system will classify according to classes mentioned above. If person don’t have any symptoms related to covid-19 means he is healthy. If he had any symptoms according to day we have classified then such type of people immediately informed and according to requirement they will be kept in isolation, quarantine or hospitalized immediately. If we able to identify during the first week of pandemic then we can break chain at early stage and we can avoid spreading of coronavirus to large number of people. If we use such type of system then no need of lockdown for long period is required. Time required only for collecting the information, identifying the infected people and take appropriate action as mentioned above. Maximum 1 week of lockdown will be sufficient and we can stop chain. No any major loss in economy. In future this type of model can be used for any type of pandemic. We will detect infected people at early stage and we can stop chain for any type of pandemic. Here we are designing this type of tool using machine learning, data mining and IoT concepts. Till date nobody has been designed such type of tool to detect covid-19 within less time.Till date nobody has been designed such type of tool to detect covid-19 within less time.

Journal of Central South University, 2017

The rapid developments in the fields of telecommunication, sensor data, financial applications, a... more The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Journal of King Saud University - Computer and Information Sciences, 2016

Data stream classification plays a vital role in data mining techniques which extracts the most i... more Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm.

International Journal of Knowledge-based and Intelligent Engineering Systems, 2016

Due to the continuous growth of recent applications such as, telecommunication, sensor data, fina... more Due to the continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task in data mining. Among the various tasks involved in data mining, the classification of data streams poses various challenging issues as compared to popular algorithms of data classification. Since the classification algorithm performs endlessly, it must be able to adapt the classification model to handle the change of concept or boundaries between classes. In order to handle these issues, we have developed a new fuzzy system called, HRFuzzy for classification of evolving data streams. Here, rough set theory and holoentropy function are utilized to construct the dynamic classification model. In the fuzzy system, the rules are generated using k-means clustering and membership functions are dynamically updated using holoentropy function. The experimentation of the proposed HRFuzzy is performed using two different databases such as, skin segmentation dataset and localization data. The performance is compared with the adaptive k-NN classifier in terms of accuracy and time. From the outcome, we proved that the proposed HRFuzzy outperformed in both the metrics by giving the maximum performance.

Stream data mining is emerging field in the data mining. Stream data generates from many applicat... more Stream data mining is emerging field in the data mining. Stream data generates from many applications such as banking, sensor networks, blogs at twitter. It is conceptually endless sequences of data records. It is often arriving at high rates. Analyzing or mining data streams raises several new issues compared to standard data mining algorithms. Standard data mining algorithms assume that records can be examined multiple times. Data stream mining algorithms, on the other hand, are more challenging to design since they must be able to extract all necessary information from records with only one pass over the data. Data stream mining algorithms must be online. Arrival rates for records are high, so the practical complexity of processing must also be low. Since the classification algorithm executes endlessly, it must be able to adapt the classification model to changes in the data stream, in particular to changes in the boundaries between classes (“concept drift”). To maximize usefulne...

THRFuzzy: Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams, 2017

The rapid developments in the fields of telecommunication, sensor data, financial applications, a... more The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams,
and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis
process consists of different tasks, among which the data stream classification approaches face more challenges than the other
commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the
classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy
classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function
helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for
the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed
THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the
evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental
results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal
time than the existing classifiers.

Data stream classification plays a vital role in data mining techniques which extracts the most i... more Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm.

The rapid developments in the fields of telecommunication, sensor data, financial applications, a... more The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

Due to the continuous growth of recent applications such as, telecommunication, sensor data, fina... more Due to the continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task in data mining. Among the various tasks involved in data mining, the classification of data streams poses various challenging issues as compared to popular algorithms of data classification. Since the classification algorithm performs endlessly, it must be able to adapt the classification model to handle the change of concept or boundaries between classes. In order to handle these issues, we have developed a new fuzzy system called, HRFuzzy for classification of evolving data streams. Here, rough set theory and holoentropy function are utilized to construct the dynamic classification model. In the fuzzy system, the rules are generated using k-means clustering and membership functions are dynamically updated using holoentropy function. The experimentation of the proposed HRFuzzy is performed using two different databases such as, skin segmentation dataset and localization data. The performance is compared with the adaptive k-NN classifier in terms of accuracy and time. From the outcome, we proved that the proposed HRFuzzy outperformed in both the metrics by giving the maximum performance.