Paper Info
Title
Exploitation of Meta-Heuristic Search Methods with Bio-Inspired Algorithms for Optimal Feature Selection
Abstract
It is very difficult and crucial to achieve the selection of optimal features, particularly for the classification task. Because the conventional method of identifying features that function independently has resulted in the selection of unrelated features, the consistency of the classification's accuracy has been degraded. The objective of this article is to optimize Meta-heuristic algorithms, particularly Tabu Search (TS) and Harmony Search (HS), using the capabilities of bioinspired search algorithms in conjunction with the wrapper. The essential stages are to idealize the TS and HS combination with appropriate bio-search methods, and to incorporate the creation of various feature subsets. The following step is to do a subset evaluation to confirm the optimum feature set. The evaluation criteria are based on the number of features utilized and the classification accuracy. To be tested, eight (8) comparison datasets of different sizes were carefully chosen. Extensive testing has indicated that the ideal combination of the chosen bio-search algorithm and meta-heuristics algorithms, especially TS and HS, promises to offer a better optimum solution (i.e. fewer features with greater classification accuracy) for the selected datasets. As a consequence of this research, the ability of bio-inspired algorithms with wrapper/filtered to select and identify characteristics would enhance the efficiency of TS and HS.
Year
DOI
Venue
2021
10.1109/IICAIET51634.2021.9573756
2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)
Keywords
DocType
ISBN
feature selection,meta-heuristics,bio-inspired,classification,tabu search,harmony search
Conference
978-1-6654-2900-9
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Mohammad Aizat Basir100.34
Mohamed Saifullah Hussin200.34