The purpose of this paper is to evaluate whether pictorial data can improve customer churn prediction and, if so, which pictures are most important. We use Random Forest and five times twofold cross-validation to analyze how much pictorial stimulus-choice data increase the AUC and top decile...

The key question of this study is: How long should the length of customer event history be for customer churn prediction? While most studies in predictive churn modeling aim to improve models by data augmentation or algorithm improvement, this study focuses on a another dimension: time window...

We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by a kernel function into a kernel matrix K. Subsequently, each K is...