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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2011.6
- 수록면
- 97 - 104 (8page)
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초록· 키워드
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Research for multi-variate classification has been studied through two kinds of procedures which are feature selection and classification. Feature Selection techniques have been applied to select important features and the other one has improved classification performances through classifier applications. In general, each technique has been independently studied, however consideration of the interaction between both procedures has not been widely explored which leads to a degraded performance. In this paper, through integrating these two procedures, classification performance can be improved. The proposed model takes advantage of KBANN (Knowledge-Based Artificial Neural Network) which uses prior knowledge to learn NN (Neural Network) as training information. Each NN learns characteristics of the Feature Selection and Classification techniques as training sets. The integrated NN can be learned again to modify features appropriately and enhance classification performance. This innovative technique is called ALBNN (Algorithm Learning-Based Neural Network). The experiments’ results show improved performance in various classification problems.
목차
1. 서론
2. 본론
3. 실험 및 결과 분석
4. 결론
참고문헌
참고문헌 (13)
참고문헌 신청
Burges, C. J. C / 1998 / A Tutorial on Support Vector Machines for Pattern Recognition / Data Mining and Knowledge Discovery 2:121~167
Cortes, C / 1995 / Support vector network / Machine Learning 20:273~297
Gori, M / 1994 / On the Problem of Local Minima in Backpropagation / IEEE Transactions on Pattern analysis and Machine Intelligence 14(1):76~86
Hagan, M. T / 1994 / Training feedforward networks with the marquardt algorithm / IEEE Transactions on Neural Networks 5(6):989~993
Kabir, M. M / 2010 / A new wrapper feature selection approach using neural network / Neruocomputing
Cortes, C / 1995 / Support vector network / Machine Learning 20:273~297
Gori, M / 1994 / On the Problem of Local Minima in Backpropagation / IEEE Transactions on Pattern analysis and Machine Intelligence 14(1):76~86
Hagan, M. T / 1994 / Training feedforward networks with the marquardt algorithm / IEEE Transactions on Neural Networks 5(6):989~993
Kabir, M. M / 2010 / A new wrapper feature selection approach using neural network / Neruocomputing
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UCI(KEPA) : I410-ECN-0101-2013-530-003614857