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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Towards application of one-class classification methods to medical data.

Itziar Irigoien1, Basilio Sierra1, Concepción Arenas2

  • 1Department of Computer Sciences and Artificial Intelligence, UPV/EHU, 20018 Donostia, Spain.

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|April 30, 2014
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Summary

A novel typicality test approach shows strong performance in one-class classification (OCC) for biomedical data. This method effectively distinguishes target classes from non-targets, even with complex, high-dimensional, and mixed data types.

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Area of Science:

  • Biomedical data analysis
  • Machine learning
  • Pattern recognition

Background:

  • One-class classification (OCC) is crucial for distinguishing a target class from all other objects in biomedical applications like diagnosis and medical data analysis.
  • Existing state-of-the-art OCC techniques face challenges with diverse data types, particularly nominal variables.

Purpose of the Study:

  • To experimentally compare a typicality test approach for one-class classification (OCC) against established methods.
  • To evaluate the performance of OCC techniques on various biomedical datasets, including those with non-continuous data.

Main Methods:

  • A typicality test-based one-class classification (OCC) method was developed and evaluated.
  • The typicality approach was compared against Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description (SVDD) methods.
  • Twelve experimental datasets with multiple classes were utilized, treating each class as the target class in turn.

Main Results:

  • The typicality test approach demonstrated good performance in one-class classification (OCC) tasks.
  • This method proved effective for high-dimensional data.
  • The typicality approach is versatile, applicable to continuous, discrete, and nominal data types.

Conclusions:

  • The typicality test offers a robust and flexible solution for one-class classification (OCC) in biomedical contexts.
  • Its ability to handle diverse data types, including nominal variables, surpasses limitations of current state-of-the-art methods.