Boolean Reasoning for Feature Extraction Problems
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Variable Precision Rough Sets with Asymmetric Bounds
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Fundamenta Informaticae
An introduction to variable and feature selection
The Journal of Machine Learning Research
Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis
IEEE Intelligent Systems
A New Rough Set Reduct Algorithm Based on Particle Swarm Optimization
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
IQuickReduct: An Improvement to Quick Reduct Algorithm
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Scalable improved quick reduct: sample based
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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IQuickReduct algorithm is an improvement over a poplar reduct computing algorithm known as QuickReduct algorithm. IQuickReduct algorithm uses variable precision rough set (VPRS) calculations as a heuristic for determining the attribute importance for selection into reduct set to resolve ambiguous situations in Quick Reduct algorithm. An apt heuristic for selecting an attribute helps in producing shorter non redundant reducts. This paper explores the selection of input attribute in ambiguous situations by adopting several heuristic approaches instead of VPRS heuristic. Extensive experimentation has been carried out on the standard datasets and the results are analyzed.