An introduction to variable and feature selection
The Journal of Machine Learning Research
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST
Journal of Classification
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A penalized criterion for variable selection in classification
Journal of Multivariate Analysis
Variable selection in model-based clustering: A general variable role modeling
Computational Statistics & Data Analysis
Tailored Aggregation for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based cluster and discriminant analysis with the MIXMOD software
Computational Statistics & Data Analysis
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A general methodology for selecting predictors for Gaussian generative classification models is presented. The problem is regarded as a model selection problem. Three different roles for each possible predictor are considered: a variable can be a relevant classification predictor or not, and the irrelevant classification variables can be linearly dependent on a part of the relevant predictors or independent variables. This variable selection model was inspired by a previous work on variable selection in model-based clustering. A BIC-like model selection criterion is proposed. It is optimized through two embedded forward stepwise variable selection algorithms for classification and linear regression. The model identifiability and the consistency of the variable selection criterion are proved. Numerical experiments on simulated and real data sets illustrate the interest of this variable selection methodology. In particular, it is shown that this well ground variable selection model can be of great interest to improve the classification performance of the quadratic discriminant analysis in a high dimension context.