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- W2378823901 abstract "The efficiency of disease pattern classification methods is often affected by feature gene selection algorithms. In this paper, the efficiency of feature gene selection algorithm based on decision tree to Support Vector Machine pattern classification was analyzed by investigating the classifiers built respectively on the gene sets of pre-feature-selection and post-feature-selection in the following four aspects: the performance of classifiers, the consistency of Support Vectors, the consistency of error samples and the stability of classifiers by equally doubling the tumor samples and the normal samples. In addition the generalization of Support Vector Machine classifiers was studied as well. The results show that the Support Vector Machine classification method not only has good adaptability to EFST algorithm but also has very strong generalization property." @default.
- W2378823901 created "2016-06-24" @default.
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- W2378823901 date "2004-01-01" @default.
- W2378823901 modified "2023-09-25" @default.
- W2378823901 title "EFFICIENCY OF FEATURE GENE SELECTION BASED ON DECISION TREE TO PATTERN CLASSIFICATION SVM" @default.
- W2378823901 hasPublicationYear "2004" @default.
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