Knowledge Extraction from L-Fuzzy Hypercontexts
نویسندگان
چکیده
As a generalization of the L-fuzzy contexts, we propose the study of the L-fuzzy hypercontexts where the relation R between the objects X and the attributes Y takes as values other L-fuzzy relations. In this work, we propose the study of these structures using OWA operators in different situations. Finally, the practical case that has motivated this paper is analyzed.
منابع مشابه
Heuristic constraints enforcement for training of and knowledge extraction from a fuzzy/neural architecture. I. Foundation
Using fuzzy/neural architectures to extract heuristic information from systems has received increasing attention. A number of fuzzy/neural architectures and knowledge extraction methods have been proposed. Knowledge extraction from systems where the existing knowledge limited is a difficult task. One of the reasons is that there is no ideal rulebase, which can be used to validate the extracted ...
متن کاملMedical Decision Making through Fuzzy Computational Intelligent Approaches
A new approach for the construction of Fuzzy Cognitive Maps augmented by knowledge through fuzzy rule-extraction methods for medical decision making is investigated. This new approach develops an augmented Fuzzy Cognitive Mapping based Decision Support System combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods...
متن کاملObjects and Goals Extraction from Semantic Networks : Applications of Fuzzy SetS Theory
In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. In this survey we address if and how some approaches met their goal.
متن کاملGreater Knowledge Extraction Based on Fuzzy Logic And GKPFCM Clustering Algorithm
This work proposes how to generate a set of fuzzy rules from a data set using a clustering algorithm, the GKPFCM. If we recommend a number of clusters, the GKPFCM identifies the location and the approximate shape of each cluster. These ones describe the relations among the variables of the data set, and they can be expressed as conditional rules such as "If/Then". The GKPFCM provides membership...
متن کاملAn Approach to Rule-Based Knowledge Extraction
The extraction of easily interpretable knowledge from the large amount of data measured in experiments is well desirable. This paper proposes a method to achieve this. A fuzzy rule system isjirst generated and optimized using evolution strategies. This fuzzy system is then converted to an RBF neural network to reJine the obtained knowledge. In order to extract understandable fuzzy rules from th...
متن کامل