Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability

• URL: http://arxiv.org/abs/2203.05749v1
• Date: Fri, 11 Mar 2022 04:31:35 GMT
• Title: Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability
• Authors: Shotaro Watanabe and Hidetoshi Matsui
• Abstract summary: We propose a new method to approach the positive and biased negative (PbN) classification problem. We incorporate a method to correct the negative impact due to skewed confidence, which represents the posterior probability that the observed data are positive.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The binary classification problem has a situation where only biased data are observed in one of the classes. In this paper, we propose a new method to approach the positive and biased negative (PbN) classification problem, which is a weakly supervised learning method to learn a binary classifier from positive data and negative data with biased observations. We incorporate a method to correct the negative impact due to skewed confidence, which represents the posterior probability that the observed data are positive. This reduces the distortion of the posterior probability that the data are labeled, which is necessary for the empirical risk minimization of the PbN classification problem. We verified the effectiveness of the proposed method by numerical experiments and real data analysis.

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