A Novel Chaotic Binary Butterfly Optimization Algorithm based Feature Selection Model for Classification of Autism Spectrum Disorder

Autism spectrum disorder (ASD) issues formidable challenges in early diagnosis and intervention, requiring efficient methods for identification and treatment. By utilizing machine learning, the risk of ASD can be accurately and promptly evaluated, thereby optimizing the analysis and expediting treat...

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Bibliographic Details
Published in:International journal of applied mathematics and computer science Vol. 34; no. 4; pp. 647 - 660
Main Authors: Ramakrishnan, Anandkumar, Ramalingam, Rajakumar, Ramalingam, Padmanaban, Ravi, Vinayakumar, Alahmadi, Tahani Jaser, Maidin, Siti Sarah
Format: Journal Article
Language:English
Published: Zielona Góra Sciendo 01.12.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Subjects:
Algorithms
Autism
autism spectrum disorder
Classification
data classification
Dimensional analysis
Feature selection
Machine learning
metaheuristics
Optimization
Optimization algorithms
ISSN:1641-876X, 2083-8492
Online Access:Get full text
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Summary:Autism spectrum disorder (ASD) issues formidable challenges in early diagnosis and intervention, requiring efficient methods for identification and treatment. By utilizing machine learning, the risk of ASD can be accurately and promptly evaluated, thereby optimizing the analysis and expediting treatment access. However, accessing high dimensional data degrades the classifier performance. In this regard, feature selection is considered an important process that enhances the classifier results. In this paper, a chaotic binary butterfly optimization algorithm based feature selection and data classification (CBBOAFS-DC) technique is proposed. It involves, preprocessing and feature selection along with data classification. Besides, a binary variant of the chaotic BOA (CBOA) is presented to choose an optimal set of a features. In addition, the CBBOAFS-DC technique employs bacterial colony optimization with a stacked sparse auto-encoder (BCO-SSAE) model for data classification. This model makes use of the BCO algorithm to optimally adjust the ‘weight’ and ‘bias’ parameters of the SSAE model to improve classification accuracy. Experiments show that the proposed scheme offers better results than benchmarked methods.
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ISSN:1641-876X
2083-8492
DOI:10.61822/amcs-2024-0043