New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures
碩士 === 國立臺灣科技大學 === 資訊工程系 === 94 === Classification techniques have been widely applied in many domains. In this thesis, we propose two new methods for handling classification problems. The first method selects feature subsets for handling classification problems based on fuzzy entropy measures focu...
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ndltd-TW-094NTUST3920042015-12-23T04:08:00Z http://ndltd.ncl.edu.tw/handle/93504009282990968312 New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures 根據模糊亂度量測法及模糊資訊增益量測法以處理分類問題之新方法 Jen-Da Shie 謝政達 碩士 國立臺灣科技大學 資訊工程系 94 Classification techniques have been widely applied in many domains. In this thesis, we propose two new methods for handling classification problems. The first method selects feature subsets for handling classification problems based on fuzzy entropy measures focusing on boundary samples. It can deal with both numeric and nominal features. It can select relevant features to get higher average classification accuracy rates than the ones selected by the existed methods. The second method handles classification problems based on fuzzy information gain measures. It can deal with both numeric and nominal features. It can get higher average classification accuracy rates than the existed methods. Shyi-Ming Chen 陳錫明 2005 學位論文 ; thesis 120 zh-TW |
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碩士 === 國立臺灣科技大學 === 資訊工程系 === 94 === Classification techniques have been widely applied in many domains. In this thesis, we propose two new methods for handling classification problems. The first method selects feature subsets for handling classification problems based on fuzzy entropy measures focusing on boundary samples. It can deal with both numeric and nominal features. It can select relevant features to get higher average classification accuracy rates than the ones selected by the existed methods. The second method handles classification problems based on fuzzy information gain measures. It can deal with both numeric and nominal features. It can get higher average classification accuracy rates than the existed methods.
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Shyi-Ming Chen |
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Shyi-Ming Chen Jen-Da Shie 謝政達 |
author |
Jen-Da Shie 謝政達 |
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Jen-Da Shie 謝政達 New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures |
author_sort |
Jen-Da Shie |
title |
New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures |
title_short |
New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures |
title_full |
New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures |
title_fullStr |
New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures |
title_full_unstemmed |
New Methods for Handling Classification Problems Based on Fuzzy Entropy Measures and Fuzzy Information Gain Measures |
title_sort |
new methods for handling classification problems based on fuzzy entropy measures and fuzzy information gain measures |
publishDate |
2005 |
url |
http://ndltd.ncl.edu.tw/handle/93504009282990968312 |
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