This paper deals with variable selection in regression and binary classification frameworks. It proposes an automatic and exhaustive procedure which relies on the use of the CART algorithm and on model selection via penalization. This work, of theoretical nature, aims at determining adequate penalties, i.e. penalties which allow achievement of oracle type inequalities justifying the performance...

A novel fully automatic Bayesian procedure for variable selection in normal regression models is proposed, along with computational strategies for model posterior evaluation. A stochastic search algorithm is given, based on the Metropolis-Hastings Algorithm, that has a stationary distribution proportional to the model posterior probabilities. The procedure is illustrated on both simulated and r...

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This paper presents a new attribute selection measure-variable attribute selection, based on an extended version of discernibility matrix. The new measure has an advantage for users to acquire knowledge of diierent levels of granu-larity, for example, discriminant rules or characteristic rules. It can also be used for handling continuous-valued attributes. Empirical results demonstrate that app...

We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research. We find latent variable model selection to be a fantastic application of matrix decomposition methods, namely, the superposition of low-rank and sparse elements. Clearly, the methodology introduced in this paper is of potential interest across many disciplines. In the followin...

In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated when used in conjunction with the lasso and orthogonal matching pursuit algor...

Model Selection is a task selecting set of potential models. This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables. In this paper, the selection of the correct number of latent variables is critical. In the most of the previous researches, the number of latent topics was selected based on the number 1 / 4

UNLABELLED Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Baye...