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Feature Selection Pitfalls and Music Classification

Previous work has employed an approach to the evaluation of wrapper feature selection methods that may overstate their ability to improve classification accuracy, because of a phenomenon akin to overfitting. This paper discusses this phenomenon in the context of recent work in machine learning, demonstrates that previous work in MIR has indeed exaggerated the efficacy of feature selection for music classification, and presents new testing providing a more realistic analysis of feature selection’s impact on music classification accuracy.