Surrogate Learning - An Approach for Semi-Supervised Classification

TL;DR

This paper introduces a semi-supervised classification method leveraging class-conditional independence to decompose the task, enabling learning from unlabeled data and demonstrating effectiveness in real-world applications.

Contribution

It presents a novel approach that uses class-conditional independence to split the classification task, facilitating semi-supervised learning from unlabeled data.

Findings

Effective semi-supervised learning demonstrated in real-world applications

Class-conditional independence enables task decomposition

Unlabeled data can be used to learn part of the classifier

Abstract

We consider the task of learning a classifier from the feature space $\mathcal{X}$ to the set of classes $\mathcal{Y} = \{0, 1\}$, when the features can be partitioned into class-conditionally independent feature sets $\mathcal{X}_1$ and $\mathcal{X}_2$. We show the surprising fact that the class-conditional independence can be used to represent the original learning task in terms of 1) learning a classifier from $\mathcal{X}_2$ to $\mathcal{X}_1$ and 2) learning the class-conditional distribution of the feature set $\mathcal{X}_1$. This fact can be exploited for semi-supervised learning because the former task can be accomplished purely from unlabeled samples. We present experimental evaluation of the idea in two real world applications.