C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ECML '93 Proceedings of the European Conference on Machine Learning
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
A Lazy Approach to Pruning Classification Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications
Learning and Intelligent Optimization
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
Association pattern mining for product specification integration
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
On the extraction of decision support rules from fuzzy predictive models
Applied Soft Computing
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Mining class association rules from dynamic class coupling data to measure class reusability pattern
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
International Journal of Applied Metaheuristic Computing
Improving classification accuracy of associative classifiers by using k-conflict-rule preservation
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Classification and association rule discovery are important data mining tasks. Using association rule discovery to construct classification systems, also known as associative classification, is a promising approach. In this paper, a new associative classification technique, Ranked Multilabel Rule (RMR) algorithm is introduced, which generates rules with multiple labels. Rules derived by current associative classification algorithms overlap in their training objects, resulting in many redundant and useless rules. However, the proposed algorithm resolves the overlapping between rules in the classifier by generating rules that does not share training objects during the training phase, resulting in a more accurate classifier. Results obtained from experimenting on 20 binary, multi-class and multi-label data sets show that the proposed technique is able to produce classifiers that contain rules associated with multiple classes. Furthermore, the results reveal that removing overlapping of training objects between the derived rules produces highly competitive classifiers if compared with those extracted by decision trees and other associative classification techniques, with respect to error rate.