Advertisement
Published in:
2011 | OriginalPaper | Chapter
Analysis of Data-Driven Parameters in Game-Theoretic Rough Sets
Authors : Joseph P. Herbert, JingTao Yao
Published in: Rough Sets and Knowledge Technology
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
The game-theoretic rough set (GTRS) model provides an alternative approach to the derivation of probabilistic rough set regions. Whereas other models rely on either user-provided parameters or notions of cost for the date set in question, the GTRS model learns these parameters through a game-theoretic process. The parameters can be of the form of probabilities that determine the rough set region bounds or they can be superseded by classification measures whose values represent the current health of the classification system. In this article, we will be analyzing the relationship between the calculated parameters and the learned values of the loss functions. We demonstrate the effectiveness of the game-theoretic rough set model in performing data analysis.