本文選題：支持向量機　切入點：決策樹　出處：《淮北師范大學(xué)》2013年碩士論文

【摘要】：隨著電信行業(yè)競爭的加劇,客戶流失分析與預(yù)測已經(jīng)成為客戶關(guān)系管理的重要內(nèi)容。電信客戶行為數(shù)據(jù)的特征呈現(xiàn)出高維度、數(shù)據(jù)偏斜、非線性。傳統(tǒng)的方法難以消除數(shù)據(jù)之間的冗余以及找到線性規(guī)律,使得預(yù)測正確率較低。同時開發(fā)系統(tǒng)過程繁雜,收益偏低,挽留客戶成本過高。 本文從以解決以上問題為出發(fā)點,主要研究基于支持向量機的客戶流失預(yù)警模型。支持向量機的算法復(fù)雜度隨著樣本數(shù)據(jù)維度和樣本總數(shù)量的增加成幾何數(shù)增長。針對這個問題,提出了一種改進的支持向量機分類方法。通過引入分類圓心、分類半徑、分類圓心距等概念,從而更加快速準確的刪除非支持向量點,引入混淆度的概念,解決了如何在樣本嚴重混淆的時候進行剔除混淆點,保證算法的泛化性。實驗證明,采用這種改進的算法能夠在嚴重混淆的訓(xùn)練樣本中保證準確度的同時提高支持向量機分類速度。 我們在Clementine數(shù)據(jù)挖掘工具平臺的基礎(chǔ)上設(shè)計了基于傳統(tǒng)支持向量機、改進支持向量機、決策樹和神經(jīng)網(wǎng)絡(luò)的客戶流失預(yù)警模型。根據(jù)實驗結(jié)果對各種分類算法進行了比較,得出了一個針對樣本數(shù)據(jù)的客戶流失原因報告。 通過本文的研究,我們解決了客戶流失預(yù)警系統(tǒng)開發(fā)費用高,預(yù)測效率低下,預(yù)測正確率不高的問題。設(shè)計了客戶預(yù)警流失模型,為企業(yè)制定挽留客戶決策提供了技術(shù)支撐。
[Abstract]:With the increasing competition in telecommunication industry, customer churn analysis and prediction has become an important part of customer relationship management. The characteristics of telecom customer behavior data show a high dimension and data skew. The traditional method is difficult to eliminate the redundancy between data and find the linear rule, which makes the prediction accuracy low. At the same time, the process of developing the system is complicated, the income is low, and the cost of retaining customers is too high. From the point of view of solving the above problems, This paper mainly studies the customer churn warning model based on support vector machine (SVM). The complexity of SVM algorithm increases with the increase of the dimension of sample data and the total number of samples. In this paper, an improved classification method of support vector machine is proposed. By introducing the concepts of the classification center, the classification radius and the classification center distance, we can delete the non-support vector points more quickly and accurately, and introduce the concept of the degree of confusion. How to eliminate the confusion points when the samples are seriously confused is solved, and the generalization of the algorithm is ensured. The improved algorithm can improve the classification speed of support vector machines while ensuring accuracy in seriously confused training samples. On the basis of Clementine data mining tool platform, we design a customer churn early warning model based on traditional support vector machine, improved support vector machine, decision tree and neural network. A customer churn report for sample data is obtained. Through the research of this paper, we solve the problems of high development cost, low prediction efficiency and low prediction accuracy of customer churn early warning system, and design customer early warning loss model, which provides technical support for enterprises to make customer retention decision.
【學(xué)位授予單位】：淮北師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP311.13;TP181

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