Integration of Kestrel-based search algorithm with artificial neural network for feature subset selection. (3rd June 2019)

• Record Type: Journal Article
• Authors: Agbehadji, Israel Edem; Millham, Richard C.; Fong, Simon James; Yang, Hongji
• Is Part Of: International journal of bio-inspired computation. Volume 13:Number 4(2019)
• Page Start: 222
• Page End: 233
• Publication Date: 2019-06-03
• Subjects: Kestrel-based search algorithm -- feature subset selection -- wrapper method -- filter method -- random encircling and imitative behaviour -- REIM -- meta-heuristic algorithm; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

Feature selection plays an important role in data pre-processing of data management. Although there are different methods available for feature selection such as filter, wrapper and embedded methods, selecting relevant features still remains a challenge in the current dispensation of big data. This paper proposes a new meta-heuristic method that integrates with wrapper method for feature subset selection. A mathematical model is formulated using random encircling and imitative behaviour (REIM) of the Kestrel bird for optimal selection of features. A test dataset from a benchmark was used to test the proposed algorithm. The performance of proposed algorithm was evaluated against PSO and ACO. The proposed model is observed to provide low error rate of 0.001143 as compared with PSO (0.0589) and ACO (0.05236). In terms of optimal size over dimension of each dataset, the proposed model performed well in 3 out of 4 datasets, while PSO-ANN performed well in 1 out of 4 datasets, ACO-ANN could not perform in any of the dataset.