Rough set algorithms in classification problem
Rough set methods and applications
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Ensembles of nested dichotomies for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Similarity Relation in Classification Problems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts
Transactions on Computational Science V
RSCTC'2010 discovery challenge: mining DNA microarray data for medical diagnosis and treatment
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
Dynamic rule-based similarity model for DNA microarray data
Transactions on Rough Sets XV
Rough sets and FCA --- scalability challenges
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Unsupervised Similarity Learning from Textual Data
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
The concept of reducts in pawlak three-step rough set analysis
Transactions on Rough Sets XVI
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We introduce the notion of a bireduct, which is an extension of the notion of a reduct developed within the theory of rough sets. For a decision system $\mathbb{A}=(U,A\cup\{d\})$ , a bireduct is a pair (B,X), where B⊆A is a subset of attributes that discerns all pairs of objects in X⊆U with different values of the decision attribute d, and where B and X cannot be, respectively, reduced and extended without losing this property. We investigate the ability of ensembles of bireducts (B,X) characterized by significant diversity with respect to both B and X to represent knowledge hidden in data and to serve as the means for learning robust classification systems. We show fundamental properties of bireducts and provide algorithms aimed at searching for ensembles of bireducts in data. We also report results obtained for some benchmark data sets.