Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Operator Learning for a Problem Class in a Distributed Peer-to-Peer Environment
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Convergence in Evolutionary Programs with Self-Adaptation
Evolutionary Computation
Optimization of Feature Processing Chain in Music Classification by Evolution Strategies
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-objective feature selection in music genre and style recognition tasks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Nearest neighbor estimate of conditional mutual information in feature selection
Expert Systems with Applications: An International Journal
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Classification of audio recordings is often based on audio-signal features. The number of available variables is usually very large. For successful categorization in e.g. genres, substyles or personal preferences small, but very predictive feature sets are sought. A further challenge is to solve this feature selection problem at least approximately with short run lengths to reduce the high computational load. We pursue this goal by applying asymmetric mutation operators in simple evolutionary strategies, which are further enhanced by mixing in greedy search operators. The resulting algorithm is reliably better than any of these approaches alone and in most cases clearly better than a deterministic greedy strategy.