Class imbalance brings significant challenges to customer churn prediction. Many solutions have been developed to address this issue. In this paper, we comprehensively compare the performance of state-of-the-art techniques to deal with class imbalance in the context of churn prediction. A recently developed expected maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost-benefit. The experimental results show that the applied evaluation metric has a great impact on the performance of techniques. An in-depth exploration of reaction patterns to different measures is conducted by intra-family comparison within each solution group and global comparison among the representative techniques from different groups. The results also indicate there is much space to improve solutions' performance in terms of profit-based measure. Our study offers valuable insights for academics and professionals and it also provides a baseline to develop new methods for dealing with class imbalance in churn prediction.

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Missing data imputation is an important research topic in data mining. The impact of noise is seldom considered in previous works while real-world data often contain much noise. In this paper, we systematically investigate the impact of noise on imputation methods and propose a new imputation approach by introducing the mechanism of Group Method of Data Handling (GMDH) to deal with incomplete data with noise. The performance of four commonly used imputation methods is compared with ours, called RIBG (robust imputation based on GMDH), on nine benchmark datasets. The experimental result demonstrates that noise has a great impact on the effectiveness of imputation techniques and our method RIBG is more robust to noise than the other four imputation methods used as benchmark.

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Class imbalance presents significant challenges to customer churn prediction. Many data-level sampling solutions have been developed to deal with this issue. In this paper, we comprehensively compare the performance of several state-of-the-art sampling techniques in the context of churn prediction. A recently developed maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost-benefit. The experimental results show that the impact of sampling methods depends on the used evaluation metric and the impact pattern is interrelated with the classifiers. An in-depth exploration of the reaction patterns is conducted and suitable sampling strategies are recommended for each situation. Furthermore, we also discuss the setting of the sampling rate in the empirical comparison. Our findings will offer a useful guideline for the use of sampling methods in the context of churn prediction.

Grape seed proanthocyanidins inhibited cholesterol esterification by miRNA-9 via targeting ACAT1 to suppress the macrophage foam cell formation.

A novel polymerizable hydrophobic monomer 1-(4-dodecyloxy-phenyl)-propenone (DPP) was synthesized by esterification, Frise rearrangement and Williamson etherification; then, the obtained DPP was copolymerized with 2-(acrylamido)-dodecanesulfonic acid (AMC

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