Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Foundations of cognitive science
A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Parameterized rough set model using rough membership and Bayesian confirmation measures
International Journal of Approximate Reasoning
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Knowledge structure, knowledge granulation and knowledge distance in a knowledge base
International Journal of Approximate Reasoning
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Fundamenta Informaticae - Advances in Rough Set Theory
Perspectives on the Field of Cognitive Informatics and its Future Development
International Journal of Cognitive Informatics and Natural Intelligence
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Multi-class decision-theoretic rough sets
International Journal of Approximate Reasoning
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
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Decision-Theoretic Rough Set DTRS model provides a three-way decision approach to classification problems, which allows a classifier to make a deferment decision on suspicious examples, rather than being forced to make an immediate determination. The deferred cases must be reexamined by collecting further information. Although the formulation of DTRS is intuitively appealing, a fundamental question that remains is how to determine the class of the deferment examples. In this paper, the authors introduce an adaptive learning method that automatically deals with the deferred examples by searching for effective granulization. A decision tree is constructed for classification. At each level, the authors sequentially choose the attributes that provide the most effective granulization. A subtree is added recursively if the conditional probability lies in between of the two given thresholds. A branch reaches its leaf node when the conditional probability is above or equal to the first threshold, or is below or equal to the second threshold, or the granule meets certain conditions. This learning process is illustrated by an example.