Inferring decision trees using the minimum description length principle
Information and Computation
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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An integrated approach of knowledge discovery has been proposed in the paper using Rough Set Theory (RST) and Dempster-Shafer's (D-S) theory where high dimensional data is reduced in two folds. Firstly, unimportant attributes are eliminated using RST generating minimal subset of attributes, called reducts. Considering each core attribute as root of a decision tree, classification rules are built and grouped based on some similarity measure. Representative of each group constitute the new rule set and thus rules has been reduced while important information are retained. D-S theory ensembles the rules from which a classifier with highest accuracy has been selected.