Classification and Novel Class Detection in Data Streams with Active Mining

We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, conceptevolution, and limited labeled data. Most of the existing data stream classification techniques address only the infinite length and conceptdrift problems. Our previous work, MineClass, addresses the conceptevolution problem in addition to addressing the infinite length and conceptdrift problems. Concept-evolution occurs in the stream when novel classes arrive. However, most of the existing data stream classification techniques, including MineClass, require that all the instances in a data stream be labeled by human experts and become available for training. This assumption is impractical, since data labeling is both time consuming and costly. Therefore, it is impossible to label a majority of the data points in a high-speed data stream. This scarcity of labeled data naturally leads to poorly trained classifiers. ActMiner actively selects only those d...

• Type: Conference
• Year: 2010
• Where: PAKDD
• Authors: Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani M. Thuraisingham
• Keywords: Data Mining, Data Stream, Data Stream Classification, PAKDD 2010, Stream Classification Techniques