Springer Science and Business Media Deutschland GmbH;Cham: Springer International Publishing
摘要:
摘要： Classification is one of the most important technologies used in data mining. Researchers have recently proposed several classification techniques based on the concept of association rules (also known as CBA-based methods). Experimental evaluations on these studies show that in average the CBA-based approaches can yield higher accuracy than some of conventional classification methods. However, conventional CBA-based methods adopt a single threshold of minimum support for all items, resulting in the rare item problem. In other words, the classification rules will only contain frequent items if minimum support ( minsup ) is set as high or any combinations of items are discovered as frequent if minsup is set as low. To solve this problem, this paper proposes a novel CBA-based method called MMSCBA, which considers the concept of multiple minimum supports (MMSs). Based on MMSs, different classification rules appear in the corresponding minsups . Several experiments were conducted with six real-world datasets selected from the UCI Machine Learning Repository. The results show that MMSCBA achieves higher accuracy than conventional CBA methods, especially when the dataset contains rare items. 其他題名： SpringerPlus 其他題名： Springerplus 出版者： Cham: Springer International Publishing 出版日期： 2016-04-26 出處： SpringerPlus, 2016-04, Vol.5 (1), p.528-528, Article 528 資源來源： Agricultural & Environmental Science Collection 版權： Hu et al. 2016 版權： The Author(s) 2016 識別號： ISSN: 2193-1801 識別號： EISSN: 2193-1801 識別號： DOI: 10.1186/s40064-016-2153-1 識別號： PMID: 27186492
