2 research outputs found
In Search of Effective Granulization with DTRS for Ternary Classification
This article was originally published by the International Journal of Cognitive Informatics and Natural IntelligenceDecision-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.NSERC Alexander Graham Bell Canada Graduate Scholarship and NSERC Canada Discovery grant