Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Boolean Reasoning for Feature Extraction Problems
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Scalable Feature Selection Using Rough Set Theory
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Fundamenta Informaticae
AI Communications - Special issue on Artificial intelligence advances in China
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
A New Approach to Distributed Algorithms for Reduct Calculation
Transactions on Rough Sets IX
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
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
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This paper develops an iterative sample based Improved Quick Reduct algorithm with Information Gain heuristic approach for recommending a quality reduct for large decision tables. The Methodology and its performance have been demonstrated by considering large datasets. It is recommended to use roughly 5 to 10% data size for obtaining an apt reduct.