Paper Info
Title
Binary social impact theory based optimization and its applications in pattern recognition
Abstract
The human opinion formation can be understood as a social approach to optimization. In the real world, the opinions on different issues encode a ''candidate solution'', which is evaluated by a complex and unknown fitness function. The computer models of such processes can be easily modified by introducing a fitness value, which leads to novel family of optimization techniques. This paper demonstrates how the novel algorithms can be derived from opinion formation models and empirically demonstrates their usability in the area of binary optimization. Particularly, it introduces a general SITO algorithmic framework and describes four algorithms based on this general framework. Recent applications of these algorithms to pattern recognition in electronic nose, electronic tongue, new born EEG and ICU patient mortality prediction are discussed. Finally, an open source SITO library for MATLAB and JAVA is introduced.
Year
DOI
Venue
2014
10.1016/j.neucom.2013.03.063
Neurocomputing
Keywords
Field
DocType
human opinion formation,optimization technique,pattern recognition,novel family,electronic nose,general sito algorithmic framework,electronic tongue,general framework,binary optimization,novel algorithm,fitness value,binary social impact theory,optimization,swarm,feature selection
ENCODE,MATLAB,Pattern recognition,Feature selection,Computer science,Usability,Multi-swarm optimization,Fitness function,Social impact theory,Artificial intelligence,Java,Machine learning
Journal
Volume
ISSN
Citations 
132,
0925-2312
5
PageRank 
References 
Authors
0.46
5
8
Name
Order
Citations
PageRank
Martin Macaš1357.84
Amol P. Bhondekar2244.78
Ritesh Kumar329337.56
Rishemjit Kaur4144.13
Jakub Kuzilek5195.16
Václav Gerla6166.69
Lenka Lhotská710930.24
Pawan Kapur8546.18