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A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection

A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection
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Author(s): Kauser Ahmed P (VIT University, India) and Senthil Kumar N (VIT University, India)
Copyright: 2018
Pages: 15
Source title: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
Source Author(s)/Editor(s): Sujata Dash (North Orissa University, India), B.K. Tripathy (VIT University, India) and Atta ur Rahman (University of Dammam, Saudi Arabia)
DOI: 10.4018/978-1-5225-2857-9.ch016

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Abstract

Due to advancement in technology, a huge volume of data is generated. Extracting knowledgeable data from this voluminous information is a difficult task. Therefore, machine learning techniques like classification, clustering, information retrieval, feature selection and data analysis has become core of recent research. These techniques can also be solved using Nature Inspired Algorithms. Nature Inspired Algorithms is inspired by processes, observed from nature. Feature Selection is helpful in finding subset of prominent components to enhance prescient precision and to expel the excess features. This chapter surveys seven nature inspired algorithms, namely Particle Swarm Optimization, Ant Colony Optimization Algorithms, Artificial Bees Colony Algorithms, Firefly Algorithms, Bat Algorithms, Cuckoo Search and Genetic Algorithms and its application in feature selections. The significance of this chapter is to present comprehensive review of nature inspired algorithms to be applied in feature selections.

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