A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data
碩士 === 國立暨南國際大學 === 資訊管理學系 === 97 === Swarm intelligence is based on observing the collective behavior of social insects and extract characteristics that can be applied to human life domains, such as ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). This pa...
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ndltd-TW-097NCNU03960422016-05-06T04:11:30Z http://ndltd.ncl.edu.tw/handle/35606502724047971739 A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data 結合核心切片反迴歸與蜜蜂演算法於支援向量機之混合式系統,以醫學資料分析為例 Ying-Zhieh Cho 卓盈玠 碩士 國立暨南國際大學 資訊管理學系 97 Swarm intelligence is based on observing the collective behavior of social insects and extract characteristics that can be applied to human life domains, such as ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). This paper proposes a hybrid model which firstly combines factor analysis (FA) with kernel sliced inverse regression (KSIR) for attribute extraction and dimensionality reduction forming the best selected feature subset. Secondly, honey-bee mating optimization (HBMO) is used to solve the problem of parameters settings in support vector machine (SVM) for classification. Results of the medical dataset from the UCI Machine Learning Repository applying the hybrid model show better results than original methods. Thus, the proposed model is an alternative and helpful scheme in analyzing medical data. Ping-Feng Pai 白炳豐 2009 學位論文 ; thesis 55 zh-TW |
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碩士 === 國立暨南國際大學 === 資訊管理學系 === 97 === Swarm intelligence is based on observing the collective behavior of social insects and extract characteristics that can be applied to human life domains, such as ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). This paper proposes a hybrid model which firstly combines factor analysis (FA) with kernel sliced inverse regression (KSIR) for attribute extraction and dimensionality reduction forming the best selected feature subset. Secondly, honey-bee mating optimization (HBMO) is used to solve the problem of parameters settings in support vector machine (SVM) for classification. Results of the medical dataset from the UCI Machine Learning Repository applying the hybrid model show better results than original methods. Thus, the proposed model is an alternative and helpful scheme in analyzing medical data.
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Ping-Feng Pai |
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Ping-Feng Pai Ying-Zhieh Cho 卓盈玠 |
author |
Ying-Zhieh Cho 卓盈玠 |
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Ying-Zhieh Cho 卓盈玠 A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data |
author_sort |
Ying-Zhieh Cho |
title |
A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data |
title_short |
A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data |
title_full |
A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data |
title_fullStr |
A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data |
title_full_unstemmed |
A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data |
title_sort |
hybrid support vectors machine model with ksir and hbmo in analyzing medical data |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/35606502724047971739 |
work_keys_str_mv |
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