Paper Info
Title
A classification and regression algorithm based on quantitative association rule tree.
Abstract
Although the association classification approach based on frequent patterns has been recently presented, the majority of the methods proposed so far do not deal with the quantitative data directly, and also do not consider the problem of exploring these rules to predict the future behavior of certain variables based on some other known variables. In light of these issues, a new algorithm based on quantitative association rules tree(CRQAR-tree) that synergizes association classification and rule-based TS fuzzy inference is developed to generate the rule tree structure and realize the classification and regression prediction. The classification and regression quantitative association rules are built on the improved Apriori algorithm which offered an efficient way for frequent itemsets learning. To manage the model complexity without sacrificing its predictive accuracy, CRQAR-tree can effectively match the rules to predict new samples that have little contribution over time. The proposed approach is applied to UCI benchmark datasets and a real application, the simulation results show that the performance of the CRQAR-tree is better than other methods, so it is a promising classification and regression algorithm.
Year
DOI
Venue
2016
10.3233/IFS-162207
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Quantitative association rule tree,classification,TS fuzzy inference,regression
Data mining,Regression,Apriori algorithm,Fuzzy inference,Algorithm,Association rule learning,Tree structure,Artificial intelligence,Mathematics,Machine learning,Model complexity
Journal
Volume
Issue
ISSN
31
3
1064-1246
Citations 
PageRank 
References 
1
0.35
12
Authors
4
Name
Order
Citations
PageRank
Ling Wang153.79
Shu-Lin Li220.71
Hua Sun310.35
Kaixiang Peng45312.22