TY - JOUR
T1 - Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection
AU - Alrasheedi, Adel Fahad
AU - Alnowibet, Khalid Abdulaziz
AU - Saxena, Akash
AU - Sallam, Karam M.
AU - Mohamed, Ali Wagdy
N1 - Funding Information:
Funding: This research is funded by the Researchers Supporting Program at King Saud University, Project number (RSP-2021/323).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Funding Information:
The authors acknowledge Kathleen Ng and Brian Karasick of the McGill Office of Sustainability for their feedback and assistance during various phases of this project. The authors also recognize Daniel Schwartz of IT Customer Services for his support in developing the online survey and managing its distribution to the McGill community. Thanks go to Guillaume Barreau for his help in calculating the travel times. The authors are also grateful to David Verbich, Dea van Lierop, and the five anonymous reviewers for the feedback received on this paper. Finally, the authors acknowledge the financial support received from the Social Sciences and Humanities Research Council and the Natural Sciences and Engineering Research Council of Canada.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.
AB - Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.
KW - classification
KW - feature selection
KW - metaheuristics
UR - http://www.scopus.com/inward/record.url?scp=85129238695&partnerID=8YFLogxK
U2 - 10.3390/math10091411
DO - 10.3390/math10091411
M3 - Article
AN - SCOPUS:85129238695
SN - 2227-7390
VL - 10
SP - 1
EP - 18
JO - Mathematics
JF - Mathematics
IS - 9
M1 - 1411
ER -