WADDAH WAHEEB | Universiti Tun Hussein Onn Malaysia (original) (raw)
Papers by WADDAH WAHEEB
Computación y Sistemas, 2017
High dimensionality of the feature space is one of the difficulty that affect short message servi... more High dimensionality of the feature space is one of the difficulty that affect short message service (SMS) classification performance. Some studies used feature selection methods to pick up some features, while other studies used the full extracted features. In this work, we aim to analyse the relationship between features size and classification performance. For that, a classification performance comparison was carried out between ten features sizes selected by varies feature selection methods. The used methods were chi-square, Gini index and information gain (IG). Support vector machine was used as a classifier. Area Under the ROC (Receiver Operating Characteristics) Curve between true positive rate and false positive rate was used to measure the classification performance. We used the repeated measures ANOVA at p < 0.05 level to analyse the performance. Experimental results showed that IG method outperformed the other methods in all features sizes. The best result was with 50% of the extracted features. Furthermore, the results explicitly showed that using larger features size in the classification does not mean superior performance but sometimes leads to less classification performance. Therefore, feature selection step should be used. By reducing the used features for the classification, without degrading the classification performance, it means reducing memory usage and classification time.
The ability to model the behaviour of arbitrary dynamic system is one of the most useful properti... more The ability to model the behaviour of arbitrary dynamic system is one of the most useful properties of recurrent networks. Dynamic ridge polynomial neural network (DRPNN) is a recurrent neural network used for time series forecasting. Despite the potential and capability of the DRPNN, stability problems could occur in the DRPNN due to the existence of the recurrent feedback. Therefore, in this study, a sufficient condition based on an approach that uses adaptive learning rate is developed by introducing a Lyapunov function. To compare the performance of the proposed solution with the existing solution, which is derived based on the stability theorem for a feedback network, we used six time series, namely Darwin sea level pressure, monthly smoothed sunspot numbers, Lorenz, Santa Fe laser, daily Euro/Dollar exchange rate and Mackey-Glass time-delay differential equation. Simulation results proved the stability of the proposed solution and showed an average 21.45% improvement in Root Mean Square Error (RMSE) with respect to the existing solution. Furthermore, the proposed solution is faster than the existing solution. This is due to the fact that the proposed solution solves network size restriction found in the existing solution and takes advantage of the calculated dynamic system variable to check the stability, unlike the existing solution that needs more calculation steps.
Time series forecasting has gained much attention due to its many practical applications. Higher-... more Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.
Time series forecasting gets much attention due to its impact on many practical applications. Hig... more Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. For that, in this paper, we propose a novel model, called Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOF), which combines three powerful properties: higher order terms, output feedback and error feedback. The well-known Mackey–Glass time series is used to evaluate the forecasting capability of RPNN-EOF. Results show that the proposed RPNN-EOF provides better understanding for the Mackey–Glass time series with root mean square error equal to 0.00416. This error is smaller than other models in the literature. Therefore, we can conclude that the RPNN-EOF can be applied successfully for time series forecasting. Furthermore, the error-output feedbacks can be investigated and applied with different neural network models. The full text of this paper can be downloaded from my HomePage.
This study presents a novel application and comparison of higher order neural networks (HONNs) to... more This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models. Keywords— Chaotic time series; Sunspot time series; Mackey-Glass time series; higher order neural network; pi-sigma neural network; functional link neural network.
Time series forecasting gets much attention due to its impact on many practical applications. Hig... more Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. In general, the most used recurrent feedback is the network output. However, no much attention has been paid to use network error instead of the network output. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that combines the properties of higher order and error feedback recurrent neural network. Three signals have been used in this paper, namely heat wave temperature, IBM common stock closing price and Mackey–Glass equation. Simulation results show that RPNN-EF is significantly faster than other RPNN-based models for one-step ahead forecasting and its forecasting performance is more significant than these models for multi-step ahead forecasting.
Content-based filtering is one of the most preferred methods to combat Short Message Service (SMS... more Content-based filtering is one of the most preferred methods to combat Short Message Service (SMS) spam. Memory usage and classification time are essential in SMS spam filtering, especially when working with limited resources. Therefore, suitable feature selection metric and proper filtering technique should be used. In this paper, we investigate how a learnt Artificial Neural Network with the Scaled Conjugate Gradient method (ANN-SCG) is suitable for content-based SMS spam filtering using a small size of features selected by Gini Index (GI) metric. The performance of ANN-SCG is evaluated in terms of true positive rate against false positive rate, Matthews Correlation Coefficient (MCC) and classification time. The evaluation results show the ability of ANN-SCG to filter SMS spam successfully with only one hundred features and a short classification time around to six microseconds. Thus, memory size and filtering time are reduced. An additional testing using unseen SMS messages is done to validate ANN-SCG with the one hundred features. The result again proves the efficiency of ANN-SCG with the one hundred features for SMS spam filtering with accuracy equal to 99.1%.
Computación y Sistemas, 2017
High dimensionality of the feature space is one of the difficulty that affect short message servi... more High dimensionality of the feature space is one of the difficulty that affect short message service (SMS) classification performance. Some studies used feature selection methods to pick up some features, while other studies used the full extracted features. In this work, we aim to analyse the relationship between features size and classification performance. For that, a classification performance comparison was carried out between ten features sizes selected by varies feature selection methods. The used methods were chi-square, Gini index and information gain (IG). Support vector machine was used as a classifier. Area Under the ROC (Receiver Operating Characteristics) Curve between true positive rate and false positive rate was used to measure the classification performance. We used the repeated measures ANOVA at p < 0.05 level to analyse the performance. Experimental results showed that IG method outperformed the other methods in all features sizes. The best result was with 50% of the extracted features. Furthermore, the results explicitly showed that using larger features size in the classification does not mean superior performance but sometimes leads to less classification performance. Therefore, feature selection step should be used. By reducing the used features for the classification, without degrading the classification performance, it means reducing memory usage and classification time.
The ability to model the behaviour of arbitrary dynamic system is one of the most useful properti... more The ability to model the behaviour of arbitrary dynamic system is one of the most useful properties of recurrent networks. Dynamic ridge polynomial neural network (DRPNN) is a recurrent neural network used for time series forecasting. Despite the potential and capability of the DRPNN, stability problems could occur in the DRPNN due to the existence of the recurrent feedback. Therefore, in this study, a sufficient condition based on an approach that uses adaptive learning rate is developed by introducing a Lyapunov function. To compare the performance of the proposed solution with the existing solution, which is derived based on the stability theorem for a feedback network, we used six time series, namely Darwin sea level pressure, monthly smoothed sunspot numbers, Lorenz, Santa Fe laser, daily Euro/Dollar exchange rate and Mackey-Glass time-delay differential equation. Simulation results proved the stability of the proposed solution and showed an average 21.45% improvement in Root Mean Square Error (RMSE) with respect to the existing solution. Furthermore, the proposed solution is faster than the existing solution. This is due to the fact that the proposed solution solves network size restriction found in the existing solution and takes advantage of the calculated dynamic system variable to check the stability, unlike the existing solution that needs more calculation steps.
Time series forecasting has gained much attention due to its many practical applications. Higher-... more Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.
Time series forecasting gets much attention due to its impact on many practical applications. Hig... more Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. For that, in this paper, we propose a novel model, called Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOF), which combines three powerful properties: higher order terms, output feedback and error feedback. The well-known Mackey–Glass time series is used to evaluate the forecasting capability of RPNN-EOF. Results show that the proposed RPNN-EOF provides better understanding for the Mackey–Glass time series with root mean square error equal to 0.00416. This error is smaller than other models in the literature. Therefore, we can conclude that the RPNN-EOF can be applied successfully for time series forecasting. Furthermore, the error-output feedbacks can be investigated and applied with different neural network models. The full text of this paper can be downloaded from my HomePage.
This study presents a novel application and comparison of higher order neural networks (HONNs) to... more This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models. Keywords— Chaotic time series; Sunspot time series; Mackey-Glass time series; higher order neural network; pi-sigma neural network; functional link neural network.
Time series forecasting gets much attention due to its impact on many practical applications. Hig... more Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting. It maintains fast learning and the ability to learn the dynamics of the series over time. In general, the most used recurrent feedback is the network output. However, no much attention has been paid to use network error instead of the network output. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that combines the properties of higher order and error feedback recurrent neural network. Three signals have been used in this paper, namely heat wave temperature, IBM common stock closing price and Mackey–Glass equation. Simulation results show that RPNN-EF is significantly faster than other RPNN-based models for one-step ahead forecasting and its forecasting performance is more significant than these models for multi-step ahead forecasting.
Content-based filtering is one of the most preferred methods to combat Short Message Service (SMS... more Content-based filtering is one of the most preferred methods to combat Short Message Service (SMS) spam. Memory usage and classification time are essential in SMS spam filtering, especially when working with limited resources. Therefore, suitable feature selection metric and proper filtering technique should be used. In this paper, we investigate how a learnt Artificial Neural Network with the Scaled Conjugate Gradient method (ANN-SCG) is suitable for content-based SMS spam filtering using a small size of features selected by Gini Index (GI) metric. The performance of ANN-SCG is evaluated in terms of true positive rate against false positive rate, Matthews Correlation Coefficient (MCC) and classification time. The evaluation results show the ability of ANN-SCG to filter SMS spam successfully with only one hundred features and a short classification time around to six microseconds. Thus, memory size and filtering time are reduced. An additional testing using unseen SMS messages is done to validate ANN-SCG with the one hundred features. The result again proves the efficiency of ANN-SCG with the one hundred features for SMS spam filtering with accuracy equal to 99.1%.