Just over thirty years after Holland first presented the outline for LearningClassifier 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 ina 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 theuse 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 ofa machine learning technique Covers a wide range of domains demonstrating theutility of the Learning Classifier Systems technique INDICE: 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 ImbalancedData with Learning Classifier Systems.- XCS for Fusing Multi-Spectral Data inAutomatic Target Recognition.- Foreign Exchange Trading using a Learning Classifier System.- Towards Clustering with Learning Classifier Systems.- Comparison of Several Genetic-Based Supervised Learning Systems.
- ISBN: 978-3-540-78978-9
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 300
- Fecha Publicación: 01/05/2008
- Nº Volúmenes: 1
- Idioma: Inglés