Mining emerging patterns by streaming feature selection

Authors:
Kui Yu;Wei Ding;Dan A. Simovici;Xindong Wu
Affiliations:
Hefei University of Technology, Hefei, China & University of Massachusetts Boston, Boston, MA, USA;University of Massachusetts Boston, Boston, MA, USA;University of Massachusetts Boston, Boston, MA, USA;Hefei University of Technology, Hefei, China & University of Vermont, Burlington, VT, USA
Venue:
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2012

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Abstract

Building an accurate emerging pattern classifier with a high-dimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unavailable before learning starts. This paper presents a new technique on mining emerging patterns using streaming feature selection. We model high feature dimensions with streaming features, that is, features arrive and are processed one at a time. As features flow in one by one, we online evaluate each coming feature to determine whether it is useful for mining predictive emerging patterns (EPs) by exploiting the relationship between feature relevance and EP discriminability (the predictive ability of an EP). We employ this relationship to guide an online EP mining process. This new approach can mine EPs from a high-dimensional dataset, even when its entire feature set is unavailable before learning. The experiments on a broad range of datasets validate the effectiveness of the proposed approach against other well-established methods, in terms of predictive accuracy, pattern numbers and running time.