Comparison Between Selective Sampling and Random Undersampling for Classification of Customer Defection Using Support Vector Machine
Heri Kuswanto, Yogi Sarumaha and Hayato Ohwada
DOI : 10.3844/jcssp.2017.355.362
Journal of Computer Science
Volume 13, Issue 8
Quality of a product determines the customer loyalty and it can be measured by conducting a survey. Company ‘X’ that sells three kinds of product (low, medium and high price) collected very large dataset through an online survey and recorded customer defection and their characteristic. The measured variables are Update Accumulation, Product Price, Customer Type, Delivery Status and Customer Defection. The data has an imbalanced response that could mislead the accuracy of classification if it is analyzed using standard approaches. Selective Sampling (SS) and Random Undersampling (RU) have been applied to draw a sample from imbalance response in order to obtain more balance data. Furthermore, Support Vector Machine (SVM) has been applied to classify the sampled data. The performance of the SS-SVM and SS-RU to classify sampled data has been evaluated and compared with the result of classifying the raw dataset. The RU yields on exact balance (50%:50%) response class, while SS reduce the imbalance proportion significantly (around 52%:48%). Nevertheless, the SS-SVM outperforms RU-SVM in the sense that it is capable to run the process effectively, where the SS-SVM reduces the duration of classification process 3 to 20 h shorter than using RU-SVM, with slightly different accuracy rate. Moreover, the SS-SVM maintains the basic characteristics of raw data better than RU-SVM.
© 2017 Heri Kuswanto, Yogi Sarumaha and Hayato Ohwada. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.