Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13934
Title: Cost-sensitive feature selection in medical data analysis with trace ratio criterion
Authors: Li, C
Shi, C
Zhang, H
Hui, C
Lam, KM 
Zhang, S
Keywords: Bayes decision theory
Cost-sensitive
Fisher score
Laplacian score
Trace ratio criterion
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: International Conference on Signal Processing Proceedings, ICSP, 2015, 7015169, p. 1077-1082 How to cite?
Abstract: 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.
Description: 2014 12th IEEE International Conference on Signal Processing, ICSP 2014, 19-23 October 2014
URI: http://hdl.handle.net/10397/13934
DOI: 10.1109/ICOSP.2014.7015169
Appears in Collections:Conference Paper

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