本文關(guān)鍵詞： 物流企業(yè) 客戶流失預(yù)測 半監(jiān)督學(xué)習(xí) 協(xié)同訓(xùn)練 非均衡數(shù)據(jù)分類　出處：《合肥工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著物流行業(yè)競爭的加劇,各物流企業(yè)提供的服務(wù)質(zhì)量越來越高,且差異越來越小,客戶的選擇也越來越多,所以客戶流失是每個(gè)物流企業(yè)都不得不面對的難題。由于客戶流失會給物流企業(yè)帶來巨大的損失,因此,客戶流失問題受到了企業(yè)界和學(xué)術(shù)界的廣泛關(guān)注。目前,已有大量的客戶流失預(yù)測方法來解決該問題,主要分為傳統(tǒng)分類方法和非均衡數(shù)據(jù)分類方法。雖然這兩種方法已經(jīng)取得了較好的預(yù)測效果,但這些方法往往需要大量的有標(biāo)記客戶數(shù)據(jù)。而在物流企業(yè)的客戶流失預(yù)測中,獲取大量的有標(biāo)記客戶數(shù)據(jù)代價(jià)昂貴。因此,如何綜合利用有標(biāo)記客戶數(shù)據(jù)和無標(biāo)記客戶數(shù)據(jù)來對物流企業(yè)的客戶流失進(jìn)行預(yù)測,成為當(dāng)前亟待解決的問題。半監(jiān)督學(xué)習(xí)是一種能夠有效利用有標(biāo)記數(shù)據(jù)和無標(biāo)記數(shù)據(jù)進(jìn)行學(xué)習(xí)的方法。因此,本研究將半監(jiān)督學(xué)習(xí)中的Co-training方法應(yīng)用到物流企業(yè)客戶流失預(yù)測問題中,來解決其存在的數(shù)據(jù)非均衡分布和獲取有標(biāo)記客戶數(shù)據(jù)代價(jià)昂貴的問題。首先,本研究系統(tǒng)分析了客戶流失預(yù)測和半監(jiān)督學(xué)習(xí)的研究現(xiàn)狀,分析了目前客戶流失預(yù)測和半監(jiān)督學(xué)習(xí)研究中存在的問題。其次,本研究對客戶流失預(yù)測、物流企業(yè)中的客戶流失預(yù)測和數(shù)據(jù)挖掘的基礎(chǔ)理論進(jìn)行了系統(tǒng)研究,分析了客戶關(guān)系管理概述、客戶流失概念及類型和客戶流失預(yù)測過程,以及物流企業(yè)客戶關(guān)系管理的特點(diǎn)、物流企業(yè)客戶流失的主要原因和物流企業(yè)客戶流失預(yù)測的兩類錯(cuò)誤,還分析了數(shù)據(jù)挖掘概述和數(shù)據(jù)挖掘中的兩類分類方法。然后,在此基礎(chǔ)上,針對客戶流失預(yù)測中存在的數(shù)據(jù)非均衡分布和半監(jiān)督學(xué)習(xí)問題,本研究將Co-training方法應(yīng)用到客戶流失預(yù)測中,并構(gòu)建了基于半監(jiān)督學(xué)習(xí)的客戶流失預(yù)測模型。最后,以物流企業(yè)為應(yīng)用背景,基于本研究提出的模型,本研究開發(fā)了面向物流企業(yè)的客戶流失預(yù)測原型系統(tǒng)。通過將本研究提出的客戶流失預(yù)測模型應(yīng)用到實(shí)際應(yīng)用場景中,來對模型的有效性和實(shí)用性進(jìn)行驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,本研究提出的模型在實(shí)際應(yīng)用中取得了較好的預(yù)測效果。通過本研究,一方面,從客戶流失預(yù)測研究中存在的問題入手,將Co-training方法應(yīng)用到客戶流失預(yù)測問題中,構(gòu)建了基于半監(jiān)督學(xué)習(xí)的客戶流失預(yù)測模型,豐富和完善了客戶流失預(yù)測的理論研究體系;另一方面,將提出的客戶流失預(yù)測模型應(yīng)用到物流企業(yè)中,開發(fā)了面向物流企業(yè)的客戶流失預(yù)測原型系統(tǒng),為準(zhǔn)確地預(yù)測物流企業(yè)的客戶流失提供了行之有效的方法。
[Abstract]:With the logistics industry competition, service quality and the logistics enterprise is higher, and the difference is more and more small, customers choose more and more, so the loss of customers is a problem that every logistics enterprises have to face. Because of the loss of customers will bring huge losses to the logistics enterprises so the customer churn problem has been widespread business circles and academic circles. At present, there are a large number of customer churn prediction method to solve this problem, mainly divided into the traditional classification methods and imbalanced data classification methods. Although these two methods have achieved good prediction results, but these methods often require a large amount of labeled data. And the customer in logistics enterprises the customer churn prediction, access to a large number of labeled customer data is expensive. Therefore, how to use customer data marked and unmarked customer data to logistics The loss prediction of enterprise customers, has become an urgent problem to solve. Semi supervised learning is an effective use of labeled data and unlabeled data to learn the method. Therefore, this study will prediction problem of semi supervised Co-training learning method applied to the logistics enterprise customer churn, to solve the existing data balanced distribution and access to customer data marked expensive problems. Firstly, this study analyzes the research status of customer churn prediction and semi supervised learning, analyzes the problems of customer churn prediction and semi supervised learning problems in the research. Secondly, the research of customer churn prediction, the basic theory of logistics enterprise customer churn prediction and data mining is studied and analyzed CRM, customer churn prediction process and loss of the concept and types of customers, and Flow characteristics of enterprise customer relationship management, two kinds of error causes of customer churn prediction of logistics enterprise and logistics enterprise customer churn, analyzes two classification methods overview of data mining and data mining. Then, on this basis, according to the customer churn prediction in the presence of data distribution and non balanced semi supervised learning problems in this study, the Co-training method is applied to the customer churn prediction, and the construction of the semi supervised learning based customer churn prediction model. Finally, taking the logistics enterprise as the background, this study based on the proposed model, this study developed for logistics enterprise customer churn prediction system. The forecast model will be applied to practical application in the scene the loss to the customer, the validity and practicability of the model is verified. The experimental results show that the application of the model proposed by this study in practice Get a better prediction result. Through this research, on the one hand, the customer churn prediction research in the existing problems, the Co-training method is applied to the prediction of customer churn, based on semi supervised learning model of customer churn prediction, enrich and improve the customer churn prediction theory research system; on the other hand, the customer churn prediction model is applied to the logistics enterprise, development oriented logistics enterprise customer churn prediction prototype system provides effective method for accurate prediction of customer churn of logistics enterprises.

【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F274;F253

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