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A Contemporarymulti–Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm Cover

A Contemporarymulti–Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm

International Journal of Applied Mathematics and Computer Science
Volume 33 (2023): Issue 1 (March 2023)
By: Santhosam Kavi Priya and  Kasirajan Pon Karthika  
Open Access
|Mar 2023

Authors

Santhosam Kavi Priya

urskavi@mepcoeng.ac.in

Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India

Kasirajan Pon Karthika

Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India
DOI: https://doi.org/10.34768/amcs-2023-0010 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 117 - 131
Submitted on: Mar 6, 2022
|
Accepted on: Jul 3, 2022
|
Published on: Mar 29, 2023
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
depression detection,
text classification,
dimensionality reduction,
hybrid feature selection,
binary gaining-sharing knowledge-based optimization
Related subjects:
Mathematics,
Applied mathematics

© 2023 Santhosam Kavi Priya, Kasirajan Pon Karthika, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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