SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts

Abstract

Abstract. This paper describes a genetic learning system called SIA, which learns attributes based rules from a set of preclassified examples. Examples may be described with a variable number of attributes, which can be numeric or symbolic, and examples may belong to several classes. SIA algorithm is somewhat similar to the AQ algorithm because it takes an example as a seed and generalizes it, using a genetic process, to find a rule maximizing a noise tolerant rule evaluation criterion. The SIA approach to supervised rule learning reduces greatly the possible rule search space when compared to the genetic Michigan and Pitt approaches. SIA is comparable to AQ and decision trees algorithms on two learning tasks. Furthermore, it has been designed for a data analysis task in a large and complex justice domain.

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

“…Moreover, the fitness function has been provided with a set of parameters so that the user can drive the process of search depending on the desired rules. The punishment of the covered instances allows the subsequent rules, found by QARGA, trying to cover those instances that were still uncovered, by means of an iterative rule learning (IRL) (Venturini 1993).…”

A Comparative Study of Several Genetic-Based Supervised Learning Systems

“…SLAVE iteratively evolves a set of individuals following an iterative rule learning scheme [47]. This process is based on the iteration of the following steps: 1) learn one rule from the dataset, 2) penalize the data covered by the rule.…”

“…As a result, the concept of GBML has been extended with new proposals of learners that use EAs to evolve their knowledge. Some of these approaches lay between the definitions of Pittsburgh-style and Michigan-style GBML, such as the Iterative Learning Rule approach [47]. Others methodologies propose to include EAs as robust search mechanisms to assist the building of neural networks [34,48] or statistical classifiers [14,36].…”

“…Iterative Rule Learning (IRL), firstly proposed in the context of the SIA system [47], uses a separate-and-conquer methodology to create an ordered list of individuals. Each individual is represented by a single rule, as in the Michigan approach.…”

Decision Queue Classifier for Supervised Learning Using Rotated Hyperboxes

“…It chooses the best individual of the evolutionary process, transforming it into a rule, which is used to eliminate data from the training file [13]. In this way, the training file is reduced for the following iteration.…”