Universiti Teknologi Malaysia Institutional Repository

Overview feature selection using fish swarm algorithm

Mohd. Rosely, N. F. L. and Salleh, R. and Zain, A. M. (2019) Overview feature selection using fish swarm algorithm. In: 2nd International Conference on Data and Information Science, ICoDIS 2018, 15-16 Nov 2018, Bandung, Indonesia.

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Official URL: http://www.dx.doi.org/10.1088/1742-6596/1192/1/012...


Feature selection is a process of representing wanted features based on the requirement needed by selecting the best subset of a dataset without changing the originality of the dataset. The aim of feature selection is to obtain most optimal feature subset to represent the data and for that purpose feature selection offered a few methods. This paper gives an easy understanding of the feature selection concept and the available methods in feature selection. As nowadays metaheuristics is catching attention researchers in many fields and feature selection is one of them, this paper intentionally brief feature selection using metaheuristics that implement Fish Swarm Algorithm (FSA) in the feature selection process. FSA classified as one of the Swarm Intelligence (SI) techniques have several advantages mainly to solve optimization problems. A number of previous works are reviewed. Based on the reviewed and the outcome results that has been tested using high dimensional, real-valued benchmark data sets, FSA reflect good performance among others SI.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:benchmarking, heuristic algorithms, optimization
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:88512
Deposited By: Narimah Nawil
Deposited On:15 Dec 2020 08:19
Last Modified:15 Dec 2020 08:19

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