Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An introduction to variable and feature selection
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel design for RNA classification using Support Vector Machines
International Journal of Data Mining and Bioinformatics
Prediction of alternatively spliced exons using Support Vector Machines
International Journal of Data Mining and Bioinformatics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Parkinson's Disease (PD) is a neurodegenerative motor system disorder, which also causes vocal impairments for most of its patients. A number of recent exploratory studies have evaluated the feasibility of detecting voice disorders by applying data mining tools to acoustic features extracted from speech recordings of patients. Selection of a minimal yet descriptive set of features is crucial for improving the classifier generalisation capability and interpretability of the classification model as well as for reducing the burden of data preprocessing. We propose a hybrid of feature selection and cross-validation procedures to lower the bias in the assessment of classifier accuracy.