Instance-Based Classification of Streaming Data Using Emerging Patterns

Abstract

Classification of Streaming Data has been recently recognized as an important research area. It is different from conventional techniques of classification because we prefer to have a single pass over each data item. Moreover, unlike conventional classification, the true labels of the data are not obtained immediately during the training process. This paper proposes ILEP, a novel instance-based technique for classification of streaming data with a modifiable reference set based on the concept of Emerging Patterns. Emerging Patterns (EPs) have been successfully used to catch important data items for addition to the reference set, hence resulting in an increase in classification accuracy as well as restricting the size of the reference set.