Distance Features in Automatic Data Classification
碩士 === 國立中央大學 === 資訊管理研究所 === 98 === In data mining and pattern classification, feature extraction and representation is a very important step since the extracted features have a direct and significant impact on the classification accuracy. In literature, numbers of novel feature extraction and repr...
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ndltd-TW-098NCU053960102016-04-20T04:17:46Z http://ndltd.ncl.edu.tw/handle/56066844700694151443 Distance Features in Automatic Data Classification 距離式特徵於資料自動分類之研究 Zhen-fu Hong 洪振富 碩士 國立中央大學 資訊管理研究所 98 In data mining and pattern classification, feature extraction and representation is a very important step since the extracted features have a direct and significant impact on the classification accuracy. In literature, numbers of novel feature extraction and representation methods have been proposed. However, many of them only focus on specific domain problems. In this thesis, we introduce a novel distance based feature extraction method for various pattern classification problems. Specifically, three distances are extracted, which are based on the distance between the data and its intra-cluster center and the distance between the data and its extra-cluster centers. Experiments based on ten datasets containing different numbers of classes, samples, and dimensions are examined. The experimental results using naïve Bayes, k-NN, and SVM classifiers show that concatenating the original features provided by the datasets to the distance based features can improve classification accuracy except image related datasets. In particular, the distance based features are suitable for the datasets which have smaller numbers of classes, numbers of samples, and the lower dimensionality of features. Moreover, two datasets, which have similar characteristics, are further used to validate this finding. The result is consistent with the first experiment result that adding the distance based features can improve the classification performance. Chih-fong Tsai 蔡志豐 2010 學位論文 ; thesis 68 zh-TW |
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碩士 === 國立中央大學 === 資訊管理研究所 === 98 === In data mining and pattern classification, feature extraction and representation is a very important step since the extracted features have a direct and significant impact on the classification accuracy. In literature, numbers of novel feature extraction and representation methods have been proposed. However, many of them only focus on specific domain problems. In this thesis, we introduce a novel distance based feature extraction method for various pattern classification problems. Specifically, three distances are extracted, which are based on the distance between the data and its intra-cluster center and the distance between the data and its extra-cluster centers. Experiments based on ten datasets containing different numbers of classes, samples, and dimensions are examined. The experimental results using naïve Bayes, k-NN, and SVM classifiers show that concatenating the original features provided by the datasets to the distance based features can improve classification accuracy except image related datasets. In particular, the distance based features are suitable for the datasets which have smaller numbers of classes, numbers of samples, and the lower dimensionality of features. Moreover, two datasets, which have similar characteristics, are further used to validate this finding. The result is consistent with the first experiment result that adding the distance based features can improve the classification performance.
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author2 |
Chih-fong Tsai |
author_facet |
Chih-fong Tsai Zhen-fu Hong 洪振富 |
author |
Zhen-fu Hong 洪振富 |
spellingShingle |
Zhen-fu Hong 洪振富 Distance Features in Automatic Data Classification |
author_sort |
Zhen-fu Hong |
title |
Distance Features in Automatic Data Classification |
title_short |
Distance Features in Automatic Data Classification |
title_full |
Distance Features in Automatic Data Classification |
title_fullStr |
Distance Features in Automatic Data Classification |
title_full_unstemmed |
Distance Features in Automatic Data Classification |
title_sort |
distance features in automatic data classification |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/56066844700694151443 |
work_keys_str_mv |
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