다변량 데이터의 분류 성능 향상을 위한 특질 추출 및 분류 기법을 통합한 신경망 알고리즘Feature Selecting and Classifying Integrated Neural Network Algorithm for Multi-variate Classification
- Other Titles
- Feature Selecting and Classifying Integrated Neural Network Algorithm for Multi-variate Classification
- Authors
- 윤현수; 백준걸
- Issue Date
- 2011
- Publisher
- 대한산업공학회
- Keywords
- classification; feature selection; data mining; neural network; KBANN; multi-variate analysis
- Citation
- 산업공학(IE interfaces), v.24, no.2, pp.97 - 104
- Indexed
- KCI
- Journal Title
- 산업공학(IE interfaces)
- Volume
- 24
- Number
- 2
- Start Page
- 97
- End Page
- 104
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/114536
- ISSN
- 1225-0996
- Abstract
- 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.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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