Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation

by   Shota Nakajima, et al.

Learning from positive and unlabeled (PU) data is an important problem in various applications. Most of the recent approaches for PU classification assume that the class-prior (the ratio of positive samples) in the training unlabeled dataset is identical to that of the test data, which does not hold in many practical cases. In addition, we usually do not know the class-priors of the training and test data, thus we have no clue on how to train a classifier without them. To address these problems, we propose a novel PU classification method based on density ratio estimation. A notable advantage of our proposed method is that it does not require the class-priors in the training phase; class-prior shift is incorporated only in the test phase. We theoretically justify our proposed method and experimentally demonstrate its effectiveness.


page 1

page 2

page 3

page 4


Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error

A bottleneck of binary classification from positive and unlabeled data (...

Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching

In real-world classification problems, the class balance in the training...

Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision

Training a classifier exploiting a huge amount of supervised data is exp...

The Hitchhiker's Guide to Prior-Shift Adaptation

In many computer vision classification tasks, class priors at test time ...

Minimising quantifier variance under prior probability shift

For the binary prevalence quantification problem under prior probability...

Class-prior Estimation for Learning from Positive and Unlabeled Data

We consider the problem of estimating the class prior in an unlabeled da...

Model Specification Test with Unlabeled Data: Approach from Covariate Shift

We propose a novel framework of the model specification test in regressi...

Please sign up or login with your details

Forgot password? Click here to reset