The ability of Learning Classifier Systems (LCS) to solve complex real-world problems is becoming clear. This book brings together work by a number of individuals who demonstrate the good performance of LCS in a variety of domains.

Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.

The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.

Brings together recent data mining applications of a machine learning technique Covers a wide range of domains demonstrating the utility of the Learning Classifier Systems technique Includes supplementary material: sn.pub/extras

Inhalt
Learning Classifier Systems in Data Mining: An Introduction.- Data Mining in Proteomics with Learning Classifier Systems.- Improving Evolutionary Computation Based Data-Mining for the Process Industry: The Importance of Abstraction.- Distributed Learning Classifier Systems.- Knowledge Discovery from Medical Data: An Empirical Study with XCS.- Mining Imbalanced Data with Learning Classifier Systems.- XCS for Fusing Multi-Spectral Data in Automatic Target Recognition.- Foreign Exchange Trading Using a Learning Classifier System.- Towards Clustering with Learning Classifier Systems.- A Comparative Study of Several Genetic-Based Supervised Learning Systems.