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Feature Selection in DNA Microarray Classification

  • Verónica Bolón-Canedo
  • Noelia Sánchez-Maroño
  • Amparo Alonso-Betanzos
Chapter
Part of the Artificial Intelligence: Foundations, Theory, and Algorithms book series (AIFTA)

Abstract

Microarray data classification is a difficult challenge for machine learning researchers due to its large number of features and small sample sizes. Since its introduction, feature selection has been considered a de facto standard in the field, and a huge number of feature selection methods were utilized trying to reduce the input dimensionality while improving the classification performance. This chapter is devoted to reviewing the most up-to-date feature selection methods developed in this field. Section 4.1 introduces the background and first attempts to deal with this type of data. Next, Section 4.2 provides a description of the inherent problematics of microarray data, such as the small sample size, the imbalance of the data, the dataset shift or the presence of outliers. In Section 4.3 we review the state of the art on feature selection methods applied to this type of data. In Section 4.4 we present an experimental study of the most significant algorithms and evaluation techniques. A deep analysis of the findings of this study is also provided (Section 4.5). Finally, in Section 4.6, we summarize the contents of this chapter.

Keywords

Feature Selection Microarray Data Feature Subset Feature Selection Method Microarray Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Verónica Bolón-Canedo
    • 1
  • Noelia Sánchez-Maroño
    • 1
  • Amparo Alonso-Betanzos
    • 1
  1. 1.Facultad de InformáticaUniversidad de A CoruñaA CoruñaSpain

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