Rough computational methods for information systems
Artificial Intelligence
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Finding the Reduct Subject to Preference Order of Attributes
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
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In data mining and knowledge discovery, the curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques, while rough set theory can be employed to reduce the dimensionality of datasets as a preprocessing step. For rough set based methods, finding reducts is an essential step, yet it is of high complexity. In this paper, based on particle swarm optimization(PSO) which is an optimization algorithm inspired by social behavior of flocks of birds when they are searching for food, a novel method is proposed for finding useful features instead of reducts in rough set theory. Subsequent experiments on UCI show that this method performs well on whole convergence, and can retrieve useful subsets effectively while retaining attributes of high importance as possible