Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/92923
標題: A hybrid optimization algorithm based on Endocrine Particle Swarm and Artificial Bee Algorithm for classification model selection
基於內分泌粒子群演算法及人造蜂群演算法之分類模型選取
作者: 謝易修
Yi-Hsiu Hsieh
關鍵字: Classification
Feature selection
Hybrid evolutionary algorithm
Particle swarm optimization
Artificial bee colony
Support Vector Machine
分類
特徵選取
混合式進化演算法
粒子群最佳化演算法
人造蜂群演算法
支援向量機
群體智慧
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摘要: The 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.
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。
URI: http://hdl.handle.net/11455/92923
其他識別: U0005-1808201516051600
文章公開時間: 2018-08-21
Appears in Collections:資訊管理學系

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