STDS: self-training data streams for mining limited labeled data in non-stationary environment

Abstract

Inthis article, wefocus on the classification problem to semi-supervised learning in non-stationary environment. Semi-supervised learning is a learning task from both labeled and unlabeled data points. There are several approaches to semi-supervised learning in stationary environment which are not applicable directly for data streams. We propose a novel semi-supervised learning algorithm, named STDS. The proposed approach uses labeled and unlabeled data and employs an approach to handle the concept drift in data streams. The main challenge in semi-supervised self-training for data streams is to find a proper selection metric in order to find a set of high-confidence predictions and a proper underlying base learner. We therefore propose an ensemble approach to find a set of high-confidence predictions based on clustering algorithms and classifier predictions. We then employ the Kullback-Leibler (KL) divergence approach to measure the distribution differences between sequential chunks in order to detect the concept drift. When drift is detected, a new classifier is updated from the new set of labeled data in the current chunk; otherwise, a percentage of high-confidence newly labeled data in the current chunk is added to the labeled data in the next chunk for updating the incremental classifier based on the proposed selection metric. The results of our experiments on a number of classification benchmark datasets show that STDS outperforms the supervised and the most of other semi-supervised learning methods.