本文選題：流失預測　切入點：非均衡二分類　出處：《中國科學技術大學》2017年碩士論文　論文類型：學位論文

【摘要】：流失預測是電信客戶關系管理的核心環(huán)節(jié),通過數(shù)據(jù)挖掘技術建模,有效預測風險客戶的流失概率,輔助運營商有針對性地設計營銷策略,為科學決策提供數(shù)據(jù)支持。通過大量文獻調研,可知電信客戶流失預測一般作為二分類問題進行研究。現(xiàn)階段該研究中面臨的關鍵科學問題如下:第一,數(shù)據(jù)集中正負樣本的非均衡分布抑制了經(jīng)典數(shù)據(jù)挖掘算法的分類性能;第二,商用大數(shù)據(jù)的隱私保護策略提高了研究工作者理解數(shù)據(jù)真實意義的難度;第三,傳統(tǒng)特征工程構建的特征總量存在上限,為模型優(yōu)化設置了瓶頸。為了克服正負樣本非均衡分布的問題,本文融合采樣技術與集成學習理論,提出非均衡組合分類器。該模型采用有放回抽樣策略構造正負樣本近似均衡的數(shù)據(jù)子集,針對數(shù)據(jù)子集訓練邏輯回歸分類器,采用投票機制累積所有分類器的預測結果,以預測結果的平均值作為集成學習模型的最終輸出。為了克服匿名特征導致的數(shù)據(jù)理解問題,本文融合數(shù)據(jù)離散化技術與獨熱編碼技巧,提出基于深度學習構造高維特征的方法。該方法通過層次化的網(wǎng)絡結構,抽取大量冗余特征,彌補加密數(shù)據(jù)難以利用領域知識和專家經(jīng)驗的缺陷。此外,借鑒決策樹模型處理非均衡分類問題的優(yōu)勢,本文將梯度提升樹模型應用于電信客戶流失預測建模,進而提出基于提升樹模型提取低維特征的方法。該方法融合集成學習理論與統(tǒng)計理論,同時實現(xiàn)預測性能的提升與計算復雜度的降低。實驗證明,本文提出的相關算法有效提升了模型的預測性能,但是由于數(shù)據(jù)集樣本容量不足,限制了部分算法的性能發(fā)揮,因此仍然存在深入研究的空間。
[Abstract]:Loss prediction is the core link of telecom customer relationship management. Through the modeling of data mining technology, it can effectively predict the probability of loss of risk customers and assist operators to design marketing strategies in a targeted way. Provide data support for scientific decision-making. Through a large number of literature research, we can see that telecom customer churn prediction is generally studied as a two-classification problem. The key scientific problems in this research are as follows: first, The disequilibrium distribution of positive and negative samples in data sets inhibits the classification performance of classical data mining algorithms. Secondly, the privacy protection strategy of commercial big data makes it more difficult for researchers to understand the real meaning of data. Third, In order to overcome the problem of non-equilibrium distribution of positive and negative samples, this paper combines sampling technology with integrated learning theory. A disequilibrium combinatorial classifier is proposed, in which a positive and negative sample approximate equalization subset is constructed by the strategy of retractable sampling, and the prediction results of all classifiers are accumulated by voting mechanism for training logical regression classifiers for data subsets. In order to overcome the problem of data understanding caused by anonymous features, the data discretization technique and the technique of single heat coding are combined in this paper, which takes the average of the predicted results as the final output of the integrated learning model. A method of constructing high dimensional features based on depth learning is proposed, in which a large number of redundant features are extracted through hierarchical network structure, which make up for the defects of domain knowledge and expert experience in encrypted data. Based on the advantage of decision tree model in dealing with the problem of disequilibrium classification, this paper applies the gradient lifting tree model to the forecasting model of telecom customer churn. Furthermore, a method of extracting low-dimensional features based on lifting tree model is proposed, which combines learning theory with statistical theory, and realizes the improvement of prediction performance and the reduction of computational complexity. The experimental results show that the proposed method can improve the prediction performance and reduce the computational complexity. The correlation algorithm proposed in this paper can effectively improve the prediction performance of the model, but there is still room for further study because of the lack of sample size in the data set, which limits the performance of some algorithms.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F274;F626

【參考文獻】

相關期刊論文 前10條

1 徐子偉;王傳啟;王鵬;黃海;;基于分步特征提取和組合分類器的電信客戶流失預測模型[J];微型機與應用;2016年13期

2 肖潔;袁嵩;譚天;;大數(shù)據(jù)時代數(shù)據(jù)隱私安全研究[J];計算機技術與發(fā)展;2016年05期

3 張潤;王永濱;;機器學習及其算法和發(fā)展研究[J];中國傳媒大學學報(自然科學版);2016年02期

4 盧宏濤;張秦川;;深度卷積神經(jīng)網(wǎng)絡在計算機視覺中的應用研究綜述[J];數(shù)據(jù)采集與處理;2016年01期

5 丁君美;劉貴全;李慧;;改進隨機森林算法在電信業(yè)客戶流失預測中的應用[J];模式識別與人工智能;2015年11期

6 王建仁;李妮;段剛龍;;基于信息融合的電信客戶流失預測研究[J];計算機工程與應用;2016年10期

7 潘文宇;蔡鑫;阮宜龍;姚曉輝;;運營商大數(shù)據(jù)平臺的敏感信息保護研究[J];電信科學;2014年11期

8 姜曉娟;郭一娜;;基于改進聚類的電信客戶流失預測分析[J];太原理工大學學報;2014年04期

9 張萌;李國喜;龔京忠;吳寶中;;Predicting configuration performance of modular product family using principal component analysis and support vector machine[J];Journal of Central South University;2014年07期

10 李勇;劉戰(zhàn)東;張海軍;;不平衡數(shù)據(jù)的集成分類算法綜述[J];計算機應用研究;2014年05期

相關博士學位論文 前2條

1 錢云;非均衡數(shù)據(jù)分類算法若干應用研究[D];吉林大學;2014年

2 夏國恩;基于商務智能的客戶流失預測模型與算法研究[D];西南交通大學;2007年

相關碩士學位論文 前1條

1 徐子偉;基于分步特征選擇和組合分類器的電信客戶流失預測模型[D];中國科學技術大學;2016年

，

本文編號：1645173