Approximate Boolean Reasoning: Foundations and Applications in Data Mining

A two-level classifier for automatic medical objects classification

“…Method 1 is a method based on the decision tree with local discretization (LLF features, the quality of a given cut is computed as a number of objects pairs discerned by this cut and belonging to different decision classes, see, e.g., [2], [3]). The method gave good results.…”

“…Our approach is based on a two-level classifier. On the lower level, our approach uses a classical classifier based on a decision tree that is calculated on the basis of the local discretization (see, e.g., [2], [3]). This classifier is constructed and based on the features extracted from images using methods known from literature (see [4], [5] for more details).…”

RSCTC’2010 Discovery Challenge: Mining DNA Microarray Data for Medical Diagnosis and Treatment

“…Firstly, according to [12], the importance of cut points can be computed. After that, the feature selection algorithm based on importance of cut points and dynamic clustering will be presented as follows.…”

Approximate Reasoning in MAS: Rough Set Approach

“…A successful methodology based on the discernibility of objects and Boolean reasoning has been developed in rough set theory for computing of many key constructs like reducts and their approximations, decision rules, association rules, discretization of real value attributes, symbolic value grouping, searching for new features defined by oblique hyperplanes or higher order surfaces, pattern extraction from data as well as conflict resolution or negotiation [51,38,44]. Most of the problems involving the computation of these entities are NP-complete or NP-hard.…”