An overview of learning in data streams with label scarcity

Abstract

Learning in data streams has practical significance in today's knowledge intensive era. Unlike static data mining, data stream mining requires handling with the critical issues related to the unbounded memory, one-scan nature, data with high arrival rate and few labels. In real nonstationary environments enormous data come with very high-speed and label scarcity. Manual labeling of such data is impractical considering requirements of expertise, time and cost. Consequently, learning in nonstationary data streams with label scarcity is being considered as a challenging task in the field of data stream mining. The present overview describes various semi-supervised learning techniques for classifying data streams with limited labeled data.