Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
Support vector machine approach for fast classification
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Effective classification by integrating support vector machine and association rule mining
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Genetic network programming for fuzzy association rule-based classification
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
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This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines. A fuzzy discretization technique is applied to transform the training set, particularly quantitative attributes, to a format appropriate for association rule mining. A hill-climbing procedure is adapted for automatic thresholds adjustment and fuzzy class association rules are mined accordingly. The compatibility between the generated rules and patterns is considered to construct a set of feature vectors, which are used to generate a classifier. The reported test results show that compatible rule-based feature vectors present a highly-qualified source of discrimination knowledge that can substantially impact the prediction power of the final classifier.