Modeling Students' Learning Bugs and Skills Using Combining Multiple Bayesian Networks
碩士 === 國立臺中教育大學 === 數學教育學系 === 94 === The goal of this paper is trying to develop fusion methods for combining multiple Bayesian networks and to obtain better classification results than single Bayesian networks. Six fusion methods, Maximum, Minimum, Average, Product, Majority Vote and Fusion Struct...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/06642102650546308286 |
| Summary: | 碩士 === 國立臺中教育大學 === 數學教育學系 === 94 === The goal of this paper is trying to develop fusion methods for combining multiple Bayesian networks and to obtain better classification results than single Bayesian networks. Six fusion methods, Maximum, Minimum, Average, Product, Majority Vote and Fusion Structure were proposed and evaluated based on educational test data. The results show that the proposed fusion methods, Structure Fusion, with dynamic cut-point selection can improve the classification accuracy.
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