Wavelet Entropy Based Probabilistic Neural Network for Classification
Khaled Daqrouq, Sheng Chen, Emad Khalaf, Ali Morfeq, Muntasir Sheikha, Abdulrohman Qatawneh, Abdulhameed AL-Khateeb
Current Journal of Applied Science and Technology · pp. 1–7 · Published 11 Apr 2019
10.9734/cjast/2019/v34i530145Abstract
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.
Cited by 2
Di Lu, Hongfeng Yu, Zhizhi Wang · Frontiers in Oncology · 2021
Showing 1 of 2 known citations — external sources report more than can currently be individually listed.
Related research
- Investigation of Diagnostic Test Performance Using Receiver Operating Characteristic and Fundamental Concepts of Information Theory — shares topic coverage
- Analysis of Steam Power Plant at Kaduna Refining and Petrochemical Company from Exergetic Perspective — shares topic coverage
- Entropy in the Analysis of Gait Complexity: A State of the Art — shares topic coverage
- Image Information Measures for Predicting Image Registration Performance on iThemba LABS Image Registration System — shares topic coverage
- Stability Constant and Thermodynamic Parameters Determination of A Semi-synthetic Penicillin Derivative with Various Bivalent Metal Ions (Co2+, Ni2+, Cu2+, Zn2+, Cd2+, Sn2+, Hg2+ and Pb2+): A Potentiometric Study — shares topic coverage
Article metrics
Real usage data collected on this platform.
11
Page views
0
PDF downloads
7
Outbound clicks
2
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.