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Customer Relationship Management usi...

Cui, Yue.

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Customer Relationship Management using Ensemble Methods.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Customer Relationship Management using Ensemble Methods./
Author:	Cui, Yue.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	62 p.
Notes:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
Subject:	Information science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792858
ISBN:	9780438185203

Customer Relationship Management using Ensemble Methods.
Cui, Yue.

Customer Relationship Management using Ensemble Methods. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 62 p.

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2018.

This thesis aims to provide a method for customer relationship management prediction to beat the in-house classification method implemented by the data provider company. By reviewing the common machine learning algorithms, we recommend ensemble methods to predict the targets. Three different ensemble methods are implemented in the thesis: random forest, gradient boosting decision trees and ensemble selection. With the results, we conclude that all ensemble methods outperform the benchmark, reduce the positive predictions and increase the true positive rates. Ensemble selection performs the best, followed by the gradient boosting decision trees. In addition, the results indicate that the ensemble methods' running time significantly increase when compared to the benchmark. The results also indicate that careful feature selection can significantly simplify the training and prediction process. We further discuss the potential applications in marketing using the prediction results and the trade-off between accuracy and computational complexity when applying ensemble methods.

ISBN: 9780438185203Subjects--Topical Terms:

554358
Information science.

Customer Relationship Management using Ensemble Methods.
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300 $a 62 p.
500 $a Source: Masters Abstracts International, Volume: 58-01.
500 $a Adviser: Joseph C. Paradi.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2018.
520 $a This thesis aims to provide a method for customer relationship management prediction to beat the in-house classification method implemented by the data provider company. By reviewing the common machine learning algorithms, we recommend ensemble methods to predict the targets. Three different ensemble methods are implemented in the thesis: random forest, gradient boosting decision trees and ensemble selection. With the results, we conclude that all ensemble methods outperform the benchmark, reduce the positive predictions and increase the true positive rates. Ensemble selection performs the best, followed by the gradient boosting decision trees. In addition, the results indicate that the ensemble methods' running time significantly increase when compared to the benchmark. The results also indicate that careful feature selection can significantly simplify the training and prediction process. We further discuss the potential applications in marketing using the prediction results and the trade-off between accuracy and computational complexity when applying ensemble methods.
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