Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification
碩士 === 國立臺灣科技大學 === 工業管理系 === 106 === This research aims to develop a data analysis framework to analyze the data generated by performing clustering and classification sequentially. The clustering and classification procedures are conducted on two different types of data, respectively: one contains...
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ndltd-TW-106NTUS50410162019-05-16T00:15:36Z http://ndltd.ncl.edu.tw/handle/ya3v2e Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification 非凌駕式排序基因演算法演化結果之分析 Shih-Chieh Tai 戴士傑 碩士 國立臺灣科技大學 工業管理系 106 This research aims to develop a data analysis framework to analyze the data generated by performing clustering and classification sequentially. The clustering and classification procedures are conducted on two different types of data, respectively: one contains the performance measure dataset denoted as Q dataset; the other contains the factors or relevant information regarding the clustering result, denoted as X dataset. Non Dominated Sorting Genetic Algorithm – Sequential Clustering Classification (NSGAII-SCC) with stepwise regression was proposed to investigate the multiple solutions generated from the proposed NSGA which aims to optimize the compactness of clustering and accuracy of classification. In this research, the stepwise regression model and frequency-based similarity matrix were applied to eliminate the redundant features in the datasets and identify the significant features for classification, respectively. The experiment result shows that the proposed methods are able to identify the redundant features and also provide the useful information for the evaluation of clustering and classification results. Chao-Lung Yang 楊朝龍 2018 學位論文 ; thesis 49 en_US |
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碩士 === 國立臺灣科技大學 === 工業管理系 === 106 === This research aims to develop a data analysis framework to analyze the data generated by performing clustering and classification sequentially. The clustering and classification procedures are conducted on two different types of data, respectively: one contains the performance measure dataset denoted as Q dataset; the other contains the factors or relevant information regarding the clustering result, denoted as X dataset. Non Dominated Sorting Genetic Algorithm – Sequential Clustering Classification (NSGAII-SCC) with stepwise regression was proposed to investigate the multiple solutions generated from the proposed NSGA which aims to optimize the compactness of clustering and accuracy of classification. In this research, the stepwise regression model and frequency-based similarity matrix were applied to eliminate the redundant features in the datasets and identify the significant features for classification, respectively. The experiment result shows that the proposed methods are able to identify the redundant features and also provide the useful information for the evaluation of clustering and classification results.
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Chao-Lung Yang |
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Chao-Lung Yang Shih-Chieh Tai 戴士傑 |
author |
Shih-Chieh Tai 戴士傑 |
spellingShingle |
Shih-Chieh Tai 戴士傑 Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification |
author_sort |
Shih-Chieh Tai |
title |
Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification |
title_short |
Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification |
title_full |
Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification |
title_fullStr |
Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification |
title_full_unstemmed |
Feature Evaluation of Non Dominated Sorting Genetic Algorithm II – Sequential Clustering Classification |
title_sort |
feature evaluation of non dominated sorting genetic algorithm ii – sequential clustering classification |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/ya3v2e |
work_keys_str_mv |
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1719163854704869376 |