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Neural Computing and Applications

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23.01.2021 | Original Article

Multi-objectives TLBO hybrid method to select the related risk features with rheumatism disease

verfasst von: Fadhaa O. Sameer, Mohammed. J. Al-obaidi, Wasan W. Al-bassam, Ali H. Ad’hiah

Erschienen in: Neural Computing and Applications | Ausgabe 15/2021

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Abstract

Features subset selection was commonly used in data mining and artificial intelligence techniques to produce a model with a minimal set of features that enhances the performance of the classifier. The essential motive for selecting features is to avoid the problem of a number of dimensions trap. This paper introduces a new technique of selection of features dependent on the modified of binary teaching–learning-based optimization and the suggested method called MBTLBO. This algorithm (teaching learning-based optimization TLBO) is one of the present metaheuristic that is been widely utilized to a several of intractable optimization issues in recent times. Such algorithm has been combined with supervised data mining technique (support vector machine) for the implementation of feature subset selection problem in binary identification. The collection of specific risk features with the rheumatic disease was implemented. The findings revealed that the new approach (MBTLBO) increases the accuracy of classification.

Vorheriger Artikel Accurate on-line support vector regression incorporated with compensated prior knowledge

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Titel: Multi-objectives TLBO hybrid method to select the related risk features with rheumatism disease
verfasst von: Fadhaa O. Sameer
Mohammed. J. Al-obaidi
Wasan W. Al-bassam
Ali H. Ad’hiah
Publikationsdatum: 23.01.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 15/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05665-1

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