From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Feature selection and effective classifiers
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
AI Communications - Special issue on Artificial intelligence advances in China
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Rough sets: trends and challenges
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A New Rough Sets Model Based on Database Systems
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
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A heuristic algorithm of reduct computation for feature selection is proposed in the paper, which is a discernibility matrix based method and aims at reducing the number of irrelevant and redundant features in data mining. The method used both significance information of attributes and information of discernibility matrix to define the necessity of heuristic feature selection. The advantage of the algorithm is that it can find an optimal reduct for feature selection in most cases. Experimental results confirmed the above assertion. It also shown that the proposed algorithm is more efficient in time performance comparing with other similar computation methods.