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

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.

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Correspondence to Fadhaa O. Sameer.

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Sameer, F.O., Al-obaidi, M.J., Al-bassam, W.W. et al. Multi-objectives TLBO hybrid method to select the related risk features with rheumatism disease. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-020-05665-1

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Keywords

  • Features selection
  • Multi-objective features selection
  • Support vector machine
  • Binary teaching learning-based optimization algorithm