Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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  • Faisal Saeed
  • Naomie Salim
  • Ibukun Omotayo Muyide

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Original languageEnglish
Article number1940
Journal / PublicationProcesses
Issue number7
Online published27 Jun 2023
Publication statusPublished - Jul 2023



Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used. © 2023 by the authors.

Research Area(s)

  • cancer classification, gene expression, machine learning, microarray data, sampling methods

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