Classification Improvement Based on Feature Combination and Topic Vector Model
碩士 === 真理大學 === 資訊工程學系碩士班 === 100 === We demonstrate a feature processing procedure which emphasizes on the combination of original features with redundancy trimming steps. This procedure shows better classification result than traditional classification models. In our experiment, several key featur...
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ndltd-TW-100AU0003920172015-10-13T21:12:10Z http://ndltd.ncl.edu.tw/handle/43119144947629463394 Classification Improvement Based on Feature Combination and Topic Vector Model 以特徵合併與隱性主題增進分類效能之研究 Chang, Yuan-Ling 張元齡 碩士 真理大學 資訊工程學系碩士班 100 We demonstrate a feature processing procedure which emphasizes on the combination of original features with redundancy trimming steps. This procedure shows better classification result than traditional classification models. In our experiment, several key feature processing steps were proposed according to the type od the feature. These steps contains numerical to categorical feature value conversion, feature combination, feature redundancy discrimination, and latent structure discovery based on the concatenation of original features and extended feature set. The UCI machine learning repository is chosen as our demonstration to show the effect of our approach. In our prelimenary resullt, it shows that the classification accuracy outperforms the traditional naive bayes classifier while the ROC benchmark equals to the naïve bayes-only scenario. This result is believed to be a promising one on the feature processing procedure research. Yeh, Jian-Hua 葉建華 2012 學位論文 ; thesis 44 zh-TW |
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碩士 === 真理大學 === 資訊工程學系碩士班 === 100 === We demonstrate a feature processing procedure which emphasizes on the combination of original features with redundancy trimming steps. This procedure shows better classification result than traditional classification models. In our experiment, several key feature processing steps were proposed according to the type od the feature. These steps contains numerical to categorical feature value conversion, feature combination, feature redundancy discrimination, and latent structure discovery based on the concatenation of original features and extended feature set. The UCI machine learning repository is chosen as our demonstration to show the effect of our approach. In our prelimenary resullt, it shows that the classification accuracy outperforms the traditional naive bayes classifier while the ROC benchmark equals to the naïve bayes-only scenario. This result is believed to be a promising one on the feature processing procedure research.
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Yeh, Jian-Hua |
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Yeh, Jian-Hua Chang, Yuan-Ling 張元齡 |
author |
Chang, Yuan-Ling 張元齡 |
spellingShingle |
Chang, Yuan-Ling 張元齡 Classification Improvement Based on Feature Combination and Topic Vector Model |
author_sort |
Chang, Yuan-Ling |
title |
Classification Improvement Based on Feature Combination and Topic Vector Model |
title_short |
Classification Improvement Based on Feature Combination and Topic Vector Model |
title_full |
Classification Improvement Based on Feature Combination and Topic Vector Model |
title_fullStr |
Classification Improvement Based on Feature Combination and Topic Vector Model |
title_full_unstemmed |
Classification Improvement Based on Feature Combination and Topic Vector Model |
title_sort |
classification improvement based on feature combination and topic vector model |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/43119144947629463394 |
work_keys_str_mv |
AT changyuanling classificationimprovementbasedonfeaturecombinationandtopicvectormodel AT zhāngyuánlíng classificationimprovementbasedonfeaturecombinationandtopicvectormodel AT changyuanling yǐtèzhēnghébìngyǔyǐnxìngzhǔtízēngjìnfēnlèixiàonéngzhīyánjiū AT zhāngyuánlíng yǐtèzhēnghébìngyǔyǐnxìngzhǔtízēngjìnfēnlèixiàonéngzhīyánjiū |
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1718057303277568000 |