In Search of Effective Granulization with DTRS for Ternary Classification

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dc.contributor.authorBing, Zhou
dc.contributor.authorYiyu, Yao
dc.date.accessioned2022-02-11T15:41:28Z
dc.date.available2022-02-11T15:41:28Z
dc.date.issued2011
dc.descriptionThis article was originally published by the International Journal of Cognitive Informatics and Natural Intelligence
dc.description.abstractDecision-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.
dc.description.sponsorshipNSERC Alexander Graham Bell Canada Graduate Scholarship and NSERC Canada Discovery grant.
dc.identifier.citationZhou, B., Yao, Y.Y. In search of effective granulization with DTRS for ternary classification. The International Journal of Cognitive Informatics & Natural Intelligence (IJCiNi). Volume 5, Issue 3, 47-60. 2011.
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3266
dc.language.isoen
dc.publisherInternational Journal of Cognitive Informatics and Natural Intelligence
dc.subjectDecision-Theoretic Rough Sets
dc.subjectGranulization
dc.subjectSubtree
dc.subjectTernary Classification
dc.subjectThree-Way Decision Approach
dc.titleIn Search of Effective Granulization with DTRS for Ternary Classification
dc.typeArticle

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