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Feature selection using mutual information based uncertainty measures for tumor classification
- Source :
- Bio-Medical Materials and Engineering. 24:763-770
- Publication Year :
- 2014
- Publisher :
- IOS Press, 2014.
-
Abstract
- Feature selection is a key problem in tumor classification and related tasks. This paper presents a tumor classification approach with neighborhood rough set-based feature selection. First, some uncertainty measures such as neighborhood entropy, conditional neighborhood entropy, neighborhood mutual information and neighborhood conditional mutual information, are in- troduced to evaluate the relevance between genes and related decision in neighborhood rough set. Then some important proper- ties and propositions of these measures are investigated, and the relationships among these measures are established as well. By using improved minimal-Redundancy-Maximal-Relevancy, combined with sequential forward greedy search strategy, a novel feature selection algorithm with low time complexity is proposed. Finally, several cancer classification tasks are demonstrated using the proposed approach. Experimental results show that the proposed algorithm is efficient and effective.
- Subjects :
- Cancer classification
Support Vector Machine
Databases, Factual
Biomedical Engineering
Feature selection
Machine learning
computer.software_genre
Biomaterials
Predictive Value of Tests
Neoplasms
Humans
Entropy (information theory)
Greedy algorithm
Time complexity
Mathematics
Computers
business.industry
Gene Expression Profiling
Conditional mutual information
Uncertainty
Computational Biology
Reproducibility of Results
General Medicine
Mutual information
Programming Languages
Rough set
Artificial intelligence
Data mining
business
computer
Algorithms
Subjects
Details
- ISSN :
- 18783619 and 09592989
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- Bio-Medical Materials and Engineering
- Accession number :
- edsair.doi.dedup.....c0e842b79e0086fc293c12a0221948e4