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Title: S-shaped vs. V-shaped transfer functions for ant lion optimization algorithm in feature selection problem
Authors: Mafarja, Majdi
Eleyan, Derar
Abdullah, Salwani
Mirjalili, Seyedali
Keywords: Selection theorems
Computational intelligence
Data mining
Machine learning
Transfer functions
Rough sets
Program transformation (Computer programming)
Ant lion optimizer
Problem solving
Separation of variables
Issue Date: 19-Jul-2017
Publisher: ACM
Citation: Majdi Mafarja, Derar Eleyan, Salwani Abdullah, and Seyedali Mirjalili. 2017. S-Shaped vs. V-Shaped Transfer Functions for Ant Lion Optimization Algorithm in Feature Selection Problem. In Proceedings of the International Conference on Future Networks and Distributed Systems (ICFNDS '17). ACM, New York, NY, USA, Article 21, 7 pages. DOI:
Abstract: Feature selection is an important preprocessing step for classification problems. It deals with selecting near optimal features in the original dataset. Feature selection is an NP-hard problem, so meta-heuristics can be more efficient than exact methods. In this work, Ant Lion Optimizer (ALO), which is a recent metaheuristic algorithm, is employed as a wrapper feature selection method. Six variants of ALO are proposed where each employ a transfer function to map a continuous search space to a discrete search space. The performance of the proposed approaches is tested on eighteen UCI datasets and compared to a number of existing approaches in the literature: Particle Swarm Optimization, Gravitational Search Algorithm and two existing ALO-based approaches. Computational experiments show that the proposed approaches efficiently explore the feature space and select the most informative features, which help to improve the classification accuracy.
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