Prediction of Customer Attrition of Commercial Banks based on SVM Model
Reads0
Chats0
TLDR
Random sampling method is used to improve SVM model, considering the imbalance characteristics of customer data sets, and the results show that this method can effectively enhance the prediction accuracy of the selected model.About:
This article is published in Procedia Computer Science.The article was published on 2014-01-01 and is currently open access. It has received 93 citations till now. The article focuses on the topics: Customer attrition & Disintermediation.read more
Citations
More filters
Journal ArticleDOI
Customer churn prediction in telecommunication industry using data certainty
TL;DR: A novel CCP approach is presented based on the above concept of classifier's certainty estimation using distance factor, which shows that the distance factor is strongly co-related with the certainty of the classifier.
Journal ArticleDOI
Digitalisation and Big Data Mining in Banking
TL;DR: This paper presents the significant progressions and most recent DM implementations in banking post 2013 and identifies the key obstacles and presents a summary for all interested parties that are facing the challenges of big data.
Journal ArticleDOI
Machine-Learning Techniques for Customer Retention: A Comparative Study
TL;DR: Results show that both random forest and ADA boost outperform all other techniques with almost the same accuracy 96%, and both Multi-layer perceptron and Support vector machine can be recommended as well with 94% accuracy.
Journal ArticleDOI
Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods
Adnan Amin,Babar Shah,Asad Masood Khattak,Fernando Moreira,Gohar Ali,Álvaro Rocha,Sajid Anwar +6 more
TL;DR: A model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) was devised and an extensive comparison was presented to validate the impact of these transformation methods in CCCC, but also evaluated the performance of underlying baseline classifiers.
Journal ArticleDOI
Churn Prediction in Customer Relationship Management via GMDH-Based Multiple Classifiers Ensemble
TL;DR: Experimental results from a novel multiple classifiers ensemble selection model based on the group method of data handling (GMDH) are encouraging.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
Chris Drummond,Robert C. Holte +1 more
TL;DR: This paper shows that using C4.5 with undersampling establishes a reasonable standard for algorithmic comparison, and it is recommended that the cheapest class classifier be part of that standard as it can be better than under-sampling for relatively modest costs.
Proceedings Article
Data mining for direct marketing: problems and solutions
Charles X. Ling,Chenghui Li +1 more
TL;DR: This paper discusses methods of coping with problems during data mining based on the experience on direct-marketing projects using data mining, and suggests a simple yet effective way of evaluating learning methods.
Journal ArticleDOI
Handling class imbalance in customer churn prediction
Jonathan Burez,D Van den Poel +1 more
TL;DR: It is found that there is no need to under-sample so that there are as many churners in your training set as non churners, and under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC.
Journal ArticleDOI
Customer churn prediction using improved balanced random forests
TL;DR: This paper proposes a novel learning method, called improved balanced random forests (IBRF), and demonstrates its application to churn prediction, and finds it to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines.