Meta-learning for Positive-unlabeled Classification

TL;DR

This paper introduces a meta-learning approach for positive-unlabeled classification that enhances classifier performance on unseen tasks by leveraging related tasks and a novel density-ratio estimation technique.

Contribution

It presents a meta-learning framework that estimates the Bayes optimal classifier using task-specific embeddings and closed-form density-ratio solutions for PU data.

Findings

Outperforms existing PU learning methods on synthetic data

Achieves better accuracy on real-world datasets

Efficiently adapts to new tasks with limited PU data

Abstract

We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target tasks. PU learning is an important problem since PU data naturally arise in real-world applications such as outlier detection and information retrieval. Existing PU learning methods require many PU data, but sufficient data are often unavailable in practice. The proposed method minimizes the test classification risk after the model is adapted to PU data by using related tasks that consist of positive, negative, and unlabeled data. We formulate the adaptation as an estimation problem of the Bayes optimal classifier, which is an optimal classifier to minimize the classification risk. The proposed method embeds each instance into a task-specific space using neural networks. With the embedded PU data, the Bayes optimal classifier is estimated through density-ratio estimation of PU densities, whose solution is obtained as a closed-form solution. The closed-form solution enables us to efficiently and effectively minimize the test classification risk. We empirically show that the proposed method outperforms existing methods with one synthetic and three real-world datasets.