Promoting Generalization in Identification Based Learning Classifier System

Generalization and Latent Learning in Learning Classifier Systems

A Simple Accuracy-based Learning Classifier System

Learning Classifier Systems use evolutionary algorithms to facilitate rule-discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of accuracy-based fitness, after the introduction of XCS, where rule fitness is based on a rule's ability to predict the expected payoff from its use. Whilst XCS has been shown to ...

Connectionist Learning Classifier System

Impetuous development of artificial neural networks makes it possible to transfer many ideas from this area into adjacent areas. This work investigates an opportunity of mapping learning classifier systems (LCS) into artificial neural networks (ANN). Possible learning types for hybrid connectionist classifier system (CLCS) for multi-step problems are derived. Transformation’s opportunity of the...

Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the ...

Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System

Biometrics consists of techniques for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits such as Iris, fingerprint, Face and Palm geometry etc. To overcome the limitations of Unimodal biometric system, a multimodal biometric is proposed. Amongst the various fusion levels, feature level fusion is expected to offer better recognition. Feature level fusion f...