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Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study.

Barbara Pes1, Giuseppina Lai1

  • 1Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Cagliari, Italy.

Peerj. Computer Science
|January 17, 2022
PubMed
Summary

This study combines feature selection and cost-sensitive learning to address high dimensionality and class imbalance in machine learning. Combining these methods shows significant benefits, especially for skewed genomic data.

Keywords:
Class imbalanceCost-sensitive learningFeature selectionHigh-dimensional data analysisRandom forest

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Area of Science:

  • Machine Learning
  • Bioinformatics
  • Data Science

Background:

  • High dimensionality and class imbalance are significant challenges in machine learning, often studied separately.
  • Existing research has not fully explored the combined effects of these issues on model generalization.
  • Genomic datasets frequently exhibit both high dimensionality and class imbalance, necessitating specialized approaches.

Purpose of the Study:

  • To comparatively evaluate machine learning strategies that integrate feature selection and cost-sensitive learning.
  • To investigate the effectiveness of different feature selection heuristics (univariate and multivariate) and cost-sensitive methods.
  • To understand the combined impact of these techniques on high-dimensional, imbalanced datasets.

Main Methods:

  • Comparative analysis of various learning strategies combining feature selection and cost-sensitive learning.
  • Exploration of different methods for incorporating misclassification costs.
  • Evaluation using univariate and multivariate feature selection heuristics on benchmark datasets.

Main Results:

  • The integration of feature selection and cost-sensitive learning yields beneficial impacts on model performance.
  • The combined approach is particularly effective for highly skewed data distributions found in genomic datasets.
  • Specific feature selection heuristics and cost-sensitive strategies demonstrate varying degrees of effectiveness.

Conclusions:

  • Combining feature selection and cost-sensitive learning is a promising strategy for tackling high-dimensional, imbalanced data.
  • This integrated approach offers improved generalization ability for machine learning models in challenging domains like genomics.
  • Further research into optimal combinations of these techniques is warranted for diverse applications.