A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare
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S.I. : HEALTHCARE ANALYTICS
A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare Madiha Tahir1 • Abdallah Tubaishat2 • Feras Al-Obeidat2 • Babar Shah2 • Zahid Halim1 Muhammad Waqas1,3
•
Received: 10 April 2020 / Accepted: 4 September 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Genetic algorithm (GA) is a nature-inspired algorithm to produce best possible solution by selecting the fittest individual from a pool of possible solutions. Like most of the optimization techniques, the GA can also stuck in the local optima, producing a suboptimal solution. This work presents a novel metaheuristic optimizer named as the binary chaotic genetic algorithm (BCGA) to improve the GA performance. The chaotic maps are applied to the initial population, and the reproduction operations follow. To demonstrate its utility, the proposed BCGA is applied to a feature selection task from an affective database, namely AMIGOS (A Dataset for Affect, Personality and Mood Research on Individuals and Groups) and two healthcare datasets having large feature space. Performance of the BCGA is compared with the traditional GA and two state-of-the-art feature selection methods. The comparison is made based on classification accuracy and the number of selected features. Experimental results suggest promising capability of BCGA to find the optimal subset of features that achieves better fitness values. The obtained results also suggest that the chaotic maps, especially sinusoidal chaotic map, perform better as compared to other maps in enhancing the performance of raw GA. The proposed approach obtains, on average, a fitness value twice as better than the one achieved through the raw GA in the identification of the seven classes of emotions. Keywords Affective computing Genetic algorithms Emotion identification Feature selection Optimization tasks Healthcare computing
& Madiha Tahir [email protected] Abdallah Tubaishat [email protected]
2
College of Technological Innovation, Zayed University, Abu Dhabi, UAE
3
Faculty of Information Technology, Beijing University of Technology, Beijing, China
Feras Al-Obeidat [email protected] Babar Shah [email protected] Zahid Halim [email protected] Muhammad Waqas [email protected] 1
The Machine Intelligence Research Group (MInG), Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
123
Neural Computing and Applications
1 Introduction The evolutionary algorithms (EAs) have recently shown promising results in solving multiple optimization problems. The EAs are powered by their stochastic search ability in multifaceted environments and are guided by one (or more) objective functions. This enables them to search the best possible solution for an optimization problem. With the advancement in information and communication technologies, coupled with
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