Combine Rough Set Theory, Support Vector Machine and the Optimization Algorithm Model in Customer Relationship Managemen
林東毅、賴?民 ; 白炳豐
E-mail: 9507395@mail.dyu.edu.tw
ABSTRACT
This research combine the Rough Set Theory, Support Vector Machine, and the Particle Swarm Optimization algorithms and apply to customer relationship management. Three steps are included ：(1) Using rough set theory identify key attributes, (2) Using Support Vector Machine to increase the classification performance of RST, (3) Using Particle Swarm Optimization search the parameters (C,σ) of Support Vector Machine. Data of the credit card questionnaire are used to verify the hybrid model. Finally, two other models, Back-Propapation Neural Network, Discriminant Analysis are used to demonstrate the classification performance of the proposed model. Experimental results show that the proposed model outperforms the other two approaches.
Keywords : customer relationship management ; rough set theory ; support vector machine ; particle swarm optimization ; back-propapation neural network ; discriminant analysis
Table of Contents
封面內頁 簽名頁 授權書 iii 中文摘要 iv ABSTRACT v 誌謝 vi 目錄 vii 圖目錄 x 表目錄 xi 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的及方法 2 1.3 研究資料 2 1.4 研究架構 2 第二章 文獻探討 4 2.1 顧客關係管理 4 2.1.1 顧客關係管理架構 4 2.1.2 顧客關係管理衡量因素 5 2.1.3 顧客關係管理範疇 7 2.2 粗略集合論 9 2.2.1 資訊系統 9 2.2.2 集合的界線 10 2.2.3 不可區別關 連 11 2.2.4 物件屬性簡化 11 2.2.5 決策表 12 2.2.6 決策法則 13 2.3 支援向量機 14 2.3.1 支援向量機原理 14 2.3.2 多類支援向 量機 19 2.4 粒子群體演算法 20 2.4.1 粒子群體演算法介紹 20 2.4.2 粒子群體演算法原理 21 2.5 倒傳遞類神經網路 22 2.5.1 倒 傳遞類神經網路介紹 22 2.5.2 倒傳遞類神經網路原理 23 2.6 區別分析 28 第三章 研究方法 30 3.1 資料前處理 31 3.2 模型建 構 31 3.3 模式比較 33 3.3.1 倒傳遞類神經網路 33 3.3.2 區別分析模型 35 第四章 案例分析 36 4.1 信用卡資料分群 36 4.2 模式 分析 37 4.2.1 結合PSO演算法與DAG模型 37 4.2.2 結合粗略集合論、PSO演算法及DAG模型 39 4.2.3 倒傳遞類神經網路模 型 42 4.2.4 區別分析模型 45 4.3 實例結果分析與討論 47 第五章 結論與未來研究方向 49 5.1 結論 49 5.2 未來研究方向 49 參 考文獻 51
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