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.
Original language | English |
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Title of host publication | International Conference on Signal Processing Proceedings, ICSP |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1077-1082 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Event | 2014 12th IEEE International Conference on Signal Processing, ICSP 2014 - Vanwarm Hotel, Hangzhou, China Duration: 19 Oct 2014 → 23 Oct 2014 |
Conference
Conference | 2014 12th IEEE International Conference on Signal Processing, ICSP 2014 |
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Country/Territory | China |
City | Hangzhou |
Period | 19/10/14 → 23/10/14 |
Keywords
- Bayes decision theory
- Cost-sensitive
- Fisher score
- Laplacian score
- Trace ratio criterion
ASJC Scopus subject areas
- Signal Processing
- Software
- Computer Science Applications