Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA (original) (raw)
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International Journal of Advance Research and Innovative Ideas in Education, 2016
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COMPARATIVE ANALYSIS OF CLASSIFICATION TECHNIQUES USING WEKA
Data Mining is the process of extracting interesting, non-trivial, implicit, previously unknown and potentially useful patterns or knowledge with the help of various techniques from various data sources. Classification is the process of finding a model that describes and distinguishes data classes or concepts. There exist several algorithms for classification in data mining, these algorithms have their strengths and weaknesses, and there is no single algorithm that is most suitable for all classes of data. This project is directed at evaluating the performance of three classification algorithms, i.e., decision tree algorithm, naïve bayes algorithm, and k-nearest Neighbour algorithm. Waikato Environment for Knowledge Analysis (WEKA) was used to analyze the algorithms; performance parameters include classification accuracy, error rate, execution time, confusion matrix, and area under the curve. Five datasets were used for the analysis, which are the Iris dataset, chronic kidney disease dataset, Breast cancer dataset, diabetes dataset, and hypothyroid dataset. The datasets were obtained from the UCI Machine Repository and split into training and testing; 60% 40% and 70% 30%. The decision tree algorithm was found to be more accurate than the naive bayes algorithm and K-NN algorithm. In terms of Execution time, K-NN outperforms naive bayes and decision trees on the five datasets. Moreover, K-NN has more percentage of error recorded on average on the five datasets. Therefore, no particular algorithm is best suited for a specific situation, the performance of classification algorithms depends on the type and size of datasets, i.e., one algorithm is more appropriate for one dataset while another algorithm is not appropriate for the same dataset.
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Data Mining refers to mining or extracting knowledge from huge volume of data. Classification is used to classify each item in set of data into one of the predefined set of classes. In data mining, an important technique is classification, generally used in broad applications, which classifies various kinds of data. In this paper, different datasets from University of California, Irvine (UCI) are compared with different classification techniques. Each technique has been evaluated with respect to accuracy and execution time and performance evaluation has been carried out with J48, Simple CART (Classification and Regression Testing), and BayesNet and NaiveBayesUpdatable Classification algorithm.
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International Journal of Electrical and Computer Engineering (IJECE), 2019
Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many processes such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. Many data users wasting a lot of time trying many classification techniques in order to find the most an appropriate technique to be used. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.
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In this paper different classification techniques of Data Mining are compared using diverse datasets from University of California, Irvine(UCI). Accuracy and time required for execution by each technique is observed. The Data Mining refers to extracting or mining knowledge from huge volume of data. Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. This work has been carried out to make a performance evaluation of J48, MultilayerPerceptron, NaiveBayesUpdatable, and BayesNet classification algorithm. Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. The paper sets out to make comparative evaluation of classifiers J48, MultilayerPerceptron, NaiveBayesUpdatable, and BayesNet in the context of Labour, Soyabean and Weather da...