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Wavelet entropy based probabilistic neural network for classification

Wavelet entropy based probabilistic neural network for classification
Wavelet entropy based probabilistic neural network for classification
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.
2457-1024
1-7
Daqrouq, Khaled
0469e0ea-e3d7-4ddb-965e-2595f4e99da0
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Khalaf, Emad
62bd08ad-df8b-4357-ac82-8b87b8c05d43
Morfeqa, Ali
4e810e14-0e23-4907-aedc-e7acf47f8eac
Sheikha, Muntasir
8dd9060d-9891-49c9-abb4-4f8483d63095
Qatawneh, Abdulrohma
c86fe852-4983-48af-9af8-96b6cc3b7239
AL-Khatee, Abdulhameed
d32bb80e-718c-411c-889a-f64732132401
Daqrouq, Khaled
0469e0ea-e3d7-4ddb-965e-2595f4e99da0
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Khalaf, Emad
62bd08ad-df8b-4357-ac82-8b87b8c05d43
Morfeqa, Ali
4e810e14-0e23-4907-aedc-e7acf47f8eac
Sheikha, Muntasir
8dd9060d-9891-49c9-abb4-4f8483d63095
Qatawneh, Abdulrohma
c86fe852-4983-48af-9af8-96b6cc3b7239
AL-Khatee, Abdulhameed
d32bb80e-718c-411c-889a-f64732132401

Daqrouq, Khaled, Chen, Sheng, Khalaf, Emad, Morfeqa, Ali, Sheikha, Muntasir, Qatawneh, Abdulrohma and AL-Khatee, Abdulhameed (2019) Wavelet entropy based probabilistic neural network for classification. Current Journal of Applied Scienceand Technology, 35 (5), 1-7. (doi:10.9734/CJAST/2019/v34i530145).

Record type: Article

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.

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Accepted/In Press date: 26 March 2019
e-pub ahead of print date: 11 April 2019

Identifiers

Local EPrints ID: 430249
URI: https://eprints.soton.ac.uk/id/eprint/430249
ISSN: 2457-1024
PURE UUID: 58d0d380-0f76-429a-8afc-cc69bc59be02

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Date deposited: 23 Apr 2019 16:30
Last modified: 23 Apr 2019 16:30

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Contributors

Author: Khaled Daqrouq
Author: Sheng Chen
Author: Emad Khalaf
Author: Ali Morfeqa
Author: Muntasir Sheikha
Author: Abdulrohma Qatawneh
Author: Abdulhameed AL-Khatee

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