Balance-Subsampled Stable Prediction

• URL: http://arxiv.org/abs/2006.04381v1
• Date: Mon, 8 Jun 2020 07:01:38 GMT
• Title: Balance-Subsampled Stable Prediction
• Authors: Kun Kuang, Hengtao Zhang, Fei Wu, Yueting Zhuang and Aijun Zhang
• Abstract summary: We propose a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. A design-theoretic analysis shows that the proposed method can reduce the confounding effects among predictors induced by the distribution shift. Numerical experiments on both synthetic and real-world data sets demonstrate that our BSSP algorithm significantly outperforms the baseline methods for stable prediction across unknown test data.
• Score: 55.13512328954456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because the sample selection bias may induce the distribution shift from training data to test data. Such a model-agnostic distribution shift usually leads to prediction instability across unknown test data. In this paper, we propose a novel balance-subsampled stable prediction (BSSP) algorithm based on the theory of fractional factorial design. It isolates the clear effect of each predictor from the confounding variables. A design-theoretic analysis shows that the proposed method can reduce the confounding effects among predictors induced by the distribution shift, hence improve both the accuracy of parameter estimation and prediction stability. Numerical experiments on both synthetic and real-world data sets demonstrate that our BSSP algorithm significantly outperforms the baseline methods for stable prediction across unknown test data.

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