Binary social impact theory based optimization and its applications in pattern recognition

Macas, Martin and Bhondekar, A.P. and Kumar, Ritesh and Kaur, Rishmjit and Kuzilek, Jakub and Gerla, Vaclav and Lhotsk, Lenka and Kapur, Pawan (2014) Binary social impact theory based optimization and its applications in pattern recognition. Neurocomputing, 132. pp. 85-96. ISSN 0925-2312

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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.

Item Type: Article
Uncontrolled Keywords: Optimization; Feature selection; Swarm; Social impact
Subjects: CSIO > Computational Instrumentation
Depositing User: Ms. Jyotsana
Date Deposited: 09 Aug 2018 12:08
Last Modified: 09 Aug 2018 12:08

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