Multiobjective Learning Classifier Systems: An Overview

Learning concept descriptions from data is a complex multiobjective task. The model induced by the learner should be accurate so that it can represent precisely the data instances, complete, which means it can be generalizable to new instances, andminimum, or easily readable. Learning Classifier Systems (LCSs) are a family or learners whose primary search mechanism is a genetic algorithm. Along the intense history of the field, the efforts of the community have been centered on the design of LCSs that solved these goals efficiently, resulting in the proposal of multiple systems. This paper revises the main LCS approaches and focuses on the analysis of the different mechanisms designed to fulfill the learning goals. Some of these mechanisms include implicit multiobjective learning mechanisms, while others use explicit multiobjective evolutionary algorithms. The paper analyses the advantages of using multiobjective evolutionary algorithms, especially in Pittsburgh LCSs, such as controlling the so-called bloat effect, and offering the human expert a set of concept description alternatives. 1 A Multiobjective Motivation Classification is a central task in data mining and machine learning applications. It consists of inducing a model that describes the target concept represented in a dataset. The dataset is formed by a set of instances, where each instance is described by a set of features and an associated class. The model describes the relation between the features and the available classes, hence it can be used to explain the hidden structure of the data set and to classify newly collected instances whose associated class is unknown. Classification may be regarded as an inherently multiobjective task. Such a task requires inducing a knowledge representation that represents the target concept completely and consistently. In many domains, the induced model should also be easily interpretable, which often means a compact representation, and take few computational resources especially in the exploitation stage but also in the training stage. All these objectives are usually opposed and classification schemes try to balance them heuristically.