Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3696
Title: Feature Selection via GM-CPSO and Binary Conversion: Analyses on a Binary-Class Dataset
Authors: Çelik, S.
Koyuncu, H.
Keywords: Binarization
Chaotic Behaviour
Feature Selection
Optimization
Pattern Classification
Wrapper Method
Classification (of information)
Nearest neighbor search
Particle swarm optimization (PSO)
Binarizations
Binary conversion
Chaotic behaviour
Chaotic particle swarm optimizations
Features selection
Gauss maps
Optimisations
Optimization method
Patterns classification
Wrapper methods
Feature Selection
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Feature selection is oft-used to upgrade the system performance in classification-based applications. For this purpose, wrapper-based methods reserve an important place and are designed with efficient optimization methods so as to observe the highest performance. In this paper, a state-of-the-art optimization method named Gauss map-based chaotic particle swarm optimization (GM-CPSO) is handled. Binary conversion is considered to adapt the GM-CPSO to the feature selection. In classification part of the proposed method, k-nearest neighborhood (k-NN) is operated due to its fast and robust performance on classification-based implementations. In experiments, seven metrics (accuracy, sensitivity, specificity, g-mean, precision, f-measure, AUC) are utilized to objectively evaluate the performances, and 80%/20% training-test split is fulfilled to effectively assign the necessary features. Our wrapper-based method is tested on a balanced dataset that is based on Parkinson's disease (PD). As a result, our method presents promising scores by means of seven metrics, and especially, it improves the classification performance about 14.59% concerning the accuracy and AUC rates in comparison with the k-NN method. © 2022 IEEE.
Description: 2022 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2022 -- 27 October 2022 through 28 October 2022 -- 185726
URI: https://doi.org/10.1109/MAJICC56935.2022.9994150
https://hdl.handle.net/20.500.13091/3696
ISBN: 9781665475198
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

Show full item record

CORE Recommender

Page view(s)

4
checked on Mar 20, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.