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dc.contributor.authorYi-Hsiu Hsiehen_US
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dc.description.abstractThe classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.en_US
dc.description.abstract數據資料的分析和分類在當今的科學研究當中是一個很重要的議題,在巨大資料中選出一些適合的特徵能夠幫助人們快速且有效率的把資料分門別類。而特徵選取可是被視為是一個特徵子集合的選擇問題,同時也是一個組合最佳化問題。 進化式演算法中在過程中使用了隨機搜尋的方法來解決最佳化問題,同時也被證實在多種應用上都有相當的成效。本篇研究提出的混合形式進化式演算法是基於內分泌粒子群演算法(Endocrine-Based Particle Swarm Optimization)及人造蜂群演算法(Artificial Bee Colony),同時結合支援向量機(Support Vector Machine)對資料集進行特徵選取與資料分類。 以UCI(University of California, Irvine)資料集進行分類結果顯示本研究提出的混合方法的搜尋精度優於內分泌粒子群與人造蜂群演算法,同時也能找出特徵數較少的特徵子集合。zh_TW
dc.description.tableofcontents摘要 i Abstract ii 目次 iii 圖片目次 v 表格目次 vi 第一章 緒論 1 1.1 研究背景與動機 1 1.2 論文架構 3 第二章 相關研究 4 2.1 支援向量機(Support Vector Machine, SVM) 4 2.2 群體智慧(Swarm Intelligence) 6 2.3 粒子群最佳化演算法(Particle Swarm Optimization, PSO) 6 2.3.1 初始化粒子群 6 2.3.2 更新粒子群個體最佳位置和全域最佳位置 7 2.3.3 更新粒子的速度、位置和參數 7 2.3.4 判斷達到終止條件 8 2.4 內分泌機制粒子群最佳化演算法(Endocrine-based Particle Swarm Optimization, EPSO) 10 2.5 人造蜂群演算法(Artificial Bee Colony, ABC) 11 2.5.1 初始化人造蜂群 11 2.5.2 工蜂階段(Employed bee phase) 12 2.5.3 觀察蜂階段(Onlooker bees phase) 13 2.5.4 偵查蜂階段(Scout bees phase) 14 2.5.5 判斷達到終止條件 15 2.6 混合式方法(Hybrid methods) 17 第三章 混合式內分泌粒子群及人造蜂群演算法 18 3.1 混合機制 18 3.2 混合型式內分泌機制粒子群及人造蜂群演算法流程 20 3.3 EPSOABC特徵選取結合支援向量機分類器 21 第四章 實驗結果 24 4.1 實驗架構與環境配備 24 4.2 演算法效能分析 27 4.2.1 特徵選取與未特徵選取比較 27 4.2.2 特徵選取和支援向量機參數搜尋效能分析 30 4.2.3 演算法分類正確率與時間之比較 32 4.3 資料集分析 39 第五章 結論與未來研究方向 43 參考文獻 44zh_TW
dc.subjectFeature selectionen_US
dc.subjectHybrid evolutionary algorithmen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectArtificial bee colonyen_US
dc.subjectSupport Vector Machineen_US
dc.titleA hybrid optimization algorithm based on Endocrine Particle Swarm and Artificial Bee Algorithm for classification model selectionen_US
dc.typeThesis and Dissertationen_US
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