Cost-sensitive feature selection in medical data analysis with trace ratio criterion

Chao Li, Cen Shi, Huan Zhang, Chun Hui, Kin Man Lam, Su Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


Feature selection and classification are important tasks in medical data mining. However, different misclassifications of medical cases could lead to different losses. This paper proposes a framework for medical data classification and relevant feature selection by the combination of the trace ratio criterion and a novel cost-sensitive linear discriminant analysis classifier approach. The proposed multi-class cost-sensitive linear discriminant analysis classifier uses linear discriminant coefficients as conditional probabilities to estimate the posterior probabilities of a testing instance, calculates misclassification losses via the posterior probabilities, and predicts the class label that minimizes losses. Experimental results showed that the proposed scheme have comparable or even lower total cost and higher accuracy than state-of-the-art cost-sensitive classification algorithm.
Original languageEnglish
Title of host publicationInternational Conference on Signal Processing Proceedings, ICSP
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
Publication statusPublished - 1 Jan 2014
Event2014 12th IEEE International Conference on Signal Processing, ICSP 2014 - Vanwarm Hotel, Hangzhou, China
Duration: 19 Oct 201423 Oct 2014


Conference2014 12th IEEE International Conference on Signal Processing, ICSP 2014


  • Bayes decision theory
  • Cost-sensitive
  • Fisher score
  • Laplacian score
  • Trace ratio criterion

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Computer Science Applications

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