An opinion formation based binary optimization approach for feature selection

Hamedmoghadam, H, Jalili, M and Yu, X 2018, 'An opinion formation based binary optimization approach for feature selection', Physica A: Statistical Mechanics and its Applications, vol. 491, pp. 142-152.


Document type: Journal Article
Collection: Journal Articles

Title An opinion formation based binary optimization approach for feature selection
Author(s) Hamedmoghadam, H
Jalili, M
Yu, X
Year 2018
Journal name Physica A: Statistical Mechanics and its Applications
Volume number 491
Start page 142
End page 152
Total pages 11
Publisher Elsevier BV
Abstract This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.
Subject Dynamical Systems in Applications
Artificial Life
Keyword(s) Complex networks
Feature selection
Opinion formation
Population-based optimization
Social dynamics
DOI - identifier 10.1016/j.physa.2017.08.048
Copyright notice © 2017 Elsevier BV
ISSN 0378-4371
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 33 Abstract Views  -  Detailed Statistics
Created: Mon, 04 Dec 2017, 10:30:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us