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
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Controlling overfitting with multi-objective support vector machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Towards timbre-invariant audio features for harmony-based music
IEEE Transactions on Audio, Speech, and Language Processing
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
Advances in Music Information Retrieval
Advances in Music Information Retrieval
Selecting small audio feature sets in music classification by means of asymmetric mutation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Feature selection for multi-purpose predictive models: a many-objective task
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Multi-objective parameters selection for SVM classification using NSGA-II
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
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Feature selection is an important prerequisite for music classification which in turn is becoming more and more ubiquitous since entering the digital music age. Automated classification into genres or even personal categories is currently envisioned even for standard mobile devices. However, classifiers often fail to work well with all available features, and simple greedy methods often fail to select good feature sets, making feature selection for music classification a natural field of application for evolutionary approaches in general, and multi-objective evolutionary algorithms in particular. In this work, we study the potential of applying such a multi-objective evolutionary optimization algorithm for feature selection with different objective sets. The result is promising, thus calling for deeper investigations of this approach.