An application of the logic of explanatory power in rough set analysis: implications for the classification of decision rules
by Anthony T. Odoemena
International Journal of Data Science (IJDS), Vol. 4, No. 2, 2019

Abstract: This paper uses the logic of explanatory power to address the question of uncertain decision rule classification and interpretation in rough set data analysis. A set theoretic configuration of the measure of explanatory power is introduced. The usefulness of the measure is then examined in the context of two datasets - one related to car evaluation and the other related to the provision of extra educational supports. It is found that the explanatory power measure has some interesting properties that enhance the informativeness and interpretation of non-deterministic decision rules. The result of the numerical analysis shows that the explanatory power index is unique. The index can also facilitate the establishment of an objective threshold that determines whether the explanatory relevance of the premise in a given decision rule is positive, negative, or neutral.

Online publication date: Wed, 26-Jun-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com