Automatically designing selection heuristics
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An incremental class boundary preserving hypersphere classifier
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
The importance of the learning conditions in hyper-heuristics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Towards a method for automatically evolving bayesian network classifiers
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
Evolutionary approach for automated component-based decision tree algorithm design
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters. The authors first offer introductory overviews on data mining, focusing on rule induction methods, and on evolutionary algorithms, focusing on genetic programming. They then examine the conventional use of evolutionary algorithms to discover classification rules or related types of knowledge structures in the data, before moving to the more ambitious objective of their research, the design of a new genetic programming system for automating the design of full rule induction algorithms. They analyze computational results from their automatically designed algorithms, which show that the machine-designed rule induction algorithms are competitive when compared with state-of-the-art human-designed algorithms. Finally the authors examine future research directions. This book will be useful for researchers and practitioners in the areas of data mining and evolutionary computation.