Learning from testing data: A new view of incremental semi-supervised learning

Abstract

In this paper, we propose a novel method for incremental semi-supervised learning. Unlike the traditional way of incremental learning or semi-supervised learning, we try to answer a more challenging question: given inadequate labeled training data, can one use the unlabeled testing data to improve the learning and prediction accuracy? The objective here is to reinforce the learning system trained offline through online incremental semi-supervised learning based on the testing data distribution. To do this, we propose an iterative algorithm that can adaptively recover the labels for testing data based on their confidence levels, and then extend the training population by such recovered data to facilitate learning and prediction. Multiple hypotheses are developed based on different learning capabilities of different recovered data sets, and a voting method is used to integrate the decisions from different hypotheses for the final predicted labels. We compare the proposed algorithm with bootstrap aggregating (bagging) method for performance evaluation. Simulation results on various real-world data sets illustrate the effectiveness of the proposed method.