Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision

• URL: http://arxiv.org/abs/2306.07036v1
• Date: Mon, 12 Jun 2023 11:33:46 GMT
• Title: Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
• Authors: Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu
• Abstract summary: We propose a new problem setting, i.e., binary classification from multiple unlabeled datasets with only one pairwise numerical relationship of class priors. In MU-OPPO, we do not need the class priors for all unlabeled datasets. We show that our framework brings smaller estimation errors of class priors and better performance of binary classification.
• Score: 128.6645627461981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high. The remarkable progress in working with weaker forms of supervision is binary classification from multiple unlabeled datasets which requires the knowledge of exact class priors for all unlabeled datasets. However, the availability of class priors is restrictive in many real-world scenarios. To address this issue, we propose to solve a new problem setting, i.e., binary classification from multiple unlabeled datasets with only one pairwise numerical relationship of class priors (MU-OPPO), which knows the relative order (which unlabeled dataset has a higher proportion of positive examples) of two class-prior probabilities for two datasets among multiple unlabeled datasets. In MU-OPPO, we do not need the class priors for all unlabeled datasets, but we only require that there exists a pair of unlabeled datasets for which we know which unlabeled dataset has a larger class prior. Clearly, this form of supervision is easier to be obtained, which can make labeling costs almost free. We propose a novel framework to handle the MU-OPPO problem, which consists of four sequential modules: (i) pseudo label assignment; (ii) confident example collection; (iii) class prior estimation; (iv) classifier training with estimated class priors. Theoretically, we analyze the gap between estimated class priors and true class priors under the proposed framework. Empirically, we confirm the superiority of our framework with comprehensive experiments. Experimental results demonstrate that our framework brings smaller estimation errors of class priors and better performance of binary classification.

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