Journal Article

Feature Selection via Chaotic Antlion Optimization

Zawbaa, Hossam M., Emary, E., Grosan, Crina

2016

Overview

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used. We propose an optimization approach for the feature selection problem that considers a \"chaotic\" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Algorithms / Analysis / Archives & records / Artificial intelligence / Bioindicators / Biology and Life Sciences / Biomarkers / Chaos theory / Classification / Computer science / Computers / Curie, Marie (1867-1934) / Data analysis / Data processing / Datasets / Ecology and Environmental Sciences / Engineering / Exploitation / Gene mapping / Genetic algorithms / Hunting / Integer programming / International conferences / Learning algorithms / Machine Learning / Mathematical problems / Mathematics / Methods / Models, Biological / Multiple objective analysis / Nonlinear Dynamics / Optimization / Physical Sciences / Prey / Random walk / Research and Analysis Methods / Time series