Adaptive Sampling Scheme for Learning in Severely Imbalanced Large Scale Data


Wei Zhang, Said Kobeissi, Scott Tomko, Chris Challis ;
Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:240-247, 2017.


Imbalanced data poses a serious challenge for many machine learning and data mining applications. It may significantly affect the performance of learning algorithms. In digital marketing applications, events of interest (positive instances for building predictive models) such as click and purchase are rare. A retail website can easily receive a million visits every day, yet only a small percentage of visits lead to purchase. The large amount of raw data and the small percentage of positive instances make it challenging to build decent predictive models in a timely fashion. In this paper, we propose an adaptive sampling strategy to deal with this problem. It efficiently returns high quality training data, ensures system responsiveness and improves predictive performances.

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