Mining emerging patterns by streaming feature selection

Building an accurate emerging pattern classifier with a highdimensional 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 w...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Kui Yu, Wei Ding 0003, Dan A. Simovici, Xindong Wu
• Keywords: Data Mining, Feature Evaluation, Feature Relevance, KDD 2012, Pattern Classifier