Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous...

Multi-Classifier Systems: Review and a roadmap for developers

Multi-Classifier Systems (MCSs) have fast been gaining popularity among researchers for their ability to fuse together multiple classification outputs for better accuracy and classification. At present, there is a lot of literature covering many of the issues and concerns that MCS designers encounters. However, we found out that there isn’t a single paper published thus far which presents an ov...

Foundations of Learning Classifier Systems: An Introduction

[Learning] Classifier systems are a kind of rule-based system with general mechanisms for processing rules in parallel, for adaptive generation of new rules, and for testing the effectiveness of existing rules. These mechanisms make possible performance and learning without the " brittleness " characteristic of most expert systems in AI.

NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS

Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...

Learning Classifier Systems in Data Mining: An Introduction