Knowledge discovery by Compensatory Fuzzy Logic predicates using a metaheuristic approach

Abstract

Compensatory Fuzzy Logic (CFL) is a logical system, which enables an optimal way for the modeling of knowledge. Its axiomatic character enables the work of natural language translation of logic, so it is used in knowledge discovery and decision-making. In this work we propose a general and flexible approach for knowledge discovery which allows obtaining different knowledge structure using a metaheuristic approach. The proposed method was tested by experimental analysis from a data set, using a tool developed in visual Prolog. The experimental results show some advantages of the proposed approach for representing patterns and trends from data.

Transdisciplinary Scientific Strategies for Soft Computing Development: Towards an Era of Data and Business Analytics

“…WKDFP have been used successfully for image treatment [103][104][105][106][107][108]. Specifically, they were created as alternatives to mathematical morphology and fuzzy morphology a new way called compensatory morphology, where the excellent robustness of CFL operators are used successfully, using new definitions of the basic morphology operators by CFL [109][110][111].…”

“…Computational Tools for their use were created, and multiple applications were made in [9,10,[89][90][91][92][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111][113][114][115][116][117][118][119][120][121][122]. A methodology for knowledge management and decision making in organizations was elaborated in [6][7][8].…”