Wavelet Entropy Based Probabilistic Neural Network for Classification
Khaled Daqrouq
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sheng Chen
Department of Electronics and Computer Sciences, University of Southampton, Southampton SO17 1BJ, UK.
Emad Khalaf *
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Ali Morfeq
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Muntasir Sheikha
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Abdulrohman Qatawneh
Higher Colleges of Technology, Sharjah, United Arab Emirates.
Abdulhameed AL-Khateeb
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
*Author to whom correspondence should be addressed.
Abstract
Recently, wavelet transform (WT) has been enormously effectual in various scientific fields. As a matter of fact, WT has overcome the FFT in the difficult nature data tackling. A wavelet entropy based probabilistic neural network (PNN) for classification applications is proposed. Specifically, wavelet transform is performed on the original input feature data, and the entropy values of the wavelet decomposition signals are then extracted to use as the input to the PNN classifier. Two benchmark data sets, Breast Cancer and Diabetes, are used to demonstrate the efficiency of our proposed wavelet entropy based PNN (WEPNN) classifier. The test classification rates of 80.3% and 77.0% are achieved respectively for the two data sets using the WEPNN with Shannon entropy. Other published methods are used for comparison. The method is promising. For results accuracy enhancement, large data set might be utilized in the future work.
Keywords: Wavelet transform, entropy, probabilistic neural network classifier.