SLAVE: a genetic learning system based on an iterative approach

Abstract

Abstract-SLAVE is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has been shown to be a useful representational tool for improving the understanding of the knowledge obtained from a human point of view. Furthermore, SLAVE uses an iterative approach for learning based on the use of a genetic algorithm (GA) as a search algorithm. In this paper, we propose a modification of the initial iterative approach used in SLAVE. The main idea is to include more information in the process of learning one individual rule. This information is included in the iterative approach through a different proposal of calculus of the positive and negative example to a rule. Furthermore, we propose the use of a new fitness function and additional genetic operators that reduce the time needed for learning and improve the understanding of the rules obtained.

A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

“…-The IRL (Iterative Rule Learning) approach, in which each chromosome represents a rule, but the solution is the best individual obtained and the global solution is formed by the best individuals obtained when the algorithm is run multiple times. SLAVE [8] is a proposal that follows this approach. -The cooperative-competitive approach, in which the complete population or a subset of it codifies the rule base.…”

“…3. SLAVE, a GA-based method for the learning of DNF fuzzy rules proposed by Gonzalez et al [8]. In [7], this method is extended by the inclusion of a feature selection process.…”

Computational Intelligence in Data Mining

“…They were the iris data [16,17], the numeral data for license plate recognition [18], the thyroid data [19], 2 the blood cell data [20], and hiragana data [12,21]. Table 5 lists the specifications of the data sets.…”

“…So, it is an important technique for extracting some hidden structures from the frequent itemsets to make the data more understandable. Closed/maximal itemset mining [23,20,21] is one of the useful method in this approach.…”

Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

“…This is a modified version of a rule evaluation criterion used in an iterative fuzzy GBML (genetics-based machine learning) algorithm called SLAVE [9]. In our heuristic rule extraction, a pre-specified number of candidate rules with the largest values of the SLAVE criterion are found for each class.…”