Titlebook: Rough Sets and Intelligent Systems Paradigms; Second International Marzena Kryszkiewicz,Chris Cornelis,Zbigniew W. Ra Conference proceeding

BAN

SQL-Based KDD with Infobright’s RDBMS: Attributes, Reducts, Trees examples of SQL-based data mining implementations. The experimental results are based on the usage of Infobright’s analytic RDBMS, whose performance characteristics perfectly fit the requirements of presented algorithms.

Wordlist

ANTI

Applications of Boolean Kernels in Rough Sets that, despite differences, there are quite many analogies in the two approaches. We will show that some rough set classifiers are in fact the SVM with Boolean kernel and propose some hybrid methods that combine the advantages of those two great machine learning approaches.

美麗的寫

Boycott

Attribute Subset Quality Functions over a Universe of Weighted Objectsbset quality functions used in our earlier research are the same function computed using two different weighting techniques. We also investigate whether it is worth using the same weights during the processes of attribute selection and new case classification.

先兆

FIR

Hybrid Model Based on Rough Sets Theory and Fuzzy Cognitive Maps for Decision-Makingive Networks. It combines the capability of Rough Sets Theory for handling inconsistent patterns, with the modeling and simulation features of Fuzzy Cognitive Maps. Towards the end, we obtain an accurate hybrid model that allows to solve non-trivial continuous, discrete, or mixed-variable decision-making problems.

Lethargic

Conference proceedings 2014 held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was held along with the 9th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2014, as a major part of the 2014 Joint Rough Set Symposium, JRS 2014. JRS 2014 received 40 revised full papers and 37 revised short

Muffle

thalamus

From Vagueness to Rough Sets in Partial Approximation Spacesness and roughness of sets via these differences. However, defining exactness/roughness of sets have some possibilities in general. In this paper, categories of vagueness, i.e., different kinds of rough sets, are identified in partial approximation spaces. Their formal definitions and intuitive meanings are given under sensible restrictions.