지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.10
- 수록면
- 1,340 - 1,344 (5page)
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초록· 키워드
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In this paper, a new approach is proposed for feature reduction using a GA-Rough hybrid approach on Bio-medical data. The given set of bio-medical data is pre-processed with the min-max normalization method. Then the subsequent evaluation on each feature with respect to the output class is carried out utilizing the information gain-based approach using the entropy-based discretization. Features with zero worth on the evaluated set of features are eliminated. The genetic algorithm is applied for performing a search for most relevant features on the set of features remained. These processes continue until there is no further change on the final reduced set of features. The rough set-based approach is applied on this set of features by applying discernibility matrix-based approach in order to obtain the final reduct. The reduced set of features, or a final reduct, is tested for classification using a TS-type rough-fuzzy classifier to show the viability of the proposed feature reduction approach. The results showed that the proposed feature reduction approach effectively achieved to reduce number of features significantly which reduced to 7 out of 120 features along with compatible classification results on the given bio-medical data compared to other approaches.
목차
Abstract
1. INTRODUCTION
2. EVALUATION ON FEATURES USING AN ENTROPY-BASED METHOD
3. GENETIC ALGORITHM FOR FEATURE SELECTION
4. ROUGH SET THEORY
5. GA- ROUGH HYBRID APPROACH FOR FEATURE REDUCTION
6. EXPERIMENTS AND RESULTS
7. CONCLUSION
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-569-000914748