Pattern Aided Classification.

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2016 SIAM International Conference on Data Mining (SDM)Pattern Aided ClassificationGuozhu Dong and Vahid TaslimitehraniGuozhu Dong and Vahid Taslimitehranipp.225 - 233Chapter DOI:https://doi.org/10.1137/1.9781611974348.26PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract This paper makes several contributions to research on classification. First, it introduces a new style of classifiers, namely pattern aided classifiers (PXC), each defined by several pattern and group-specific-classifier pairs. A PXC uses patterns as conditions and it applies a group-specific classifier only to data instances satisfying its associated pattern. Second, it introduces a new classification algorithm, called Contrast Pattern Aided Classification (CPXC), for learning accurate PXCs. Experiments over multiple benchmark datasets confirm that CPXC often builds significantly more accurate classifiers than traditional classification algorithms. Third, it introduces the technique of opportunity-guided boosting and the concept of conditional classifier ensembles, and it provides insight on why certain datasets are very challenging to traditional classification algorithms. Previous chapter Next chapter RelatedDetails Published:2016eISBN:978-1-61197-434-8 https://doi.org/10.1137/1.9781611974348Book Series Name:ProceedingsBook Code:PRDT16Book Pages:1-867Key words:classification algorithm, statistical learning, supervised learning, boosting, pattern analysis, association rules