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In this paper, we propose another extension of the Random Forests paradigm to unlabeled data, leading to localized unsupervised feature selection (FS). We show that the way internal estimates are used to measure variable importance in Random Forests are also applicable to FS in unsupervised learning. We first illustrate the clustering performance of the proposed method on various data sets based on...
This paper introduces a novel conservative feature subset selection method with incomplete data sets. The method is conservative in the sense that it selects the minimal subset of features that renders the rest of the features independent of the target (the class variable) without making any assumption about the missing data mechanism. This is achieved in the context of determining the Markov blanket...
This paper presents a diagnosis system for detecting tramway rollers defects. First, the continuous wavelet transform is applied on vibration signals measured by specific accelerometers. Then, the Singular Values Decomposition (SVD) is applied on the time-scale representations to extract a set of singular values as classification features. The resulting multi-class classification problem is decomposed...
In this paper, we propose a new constraint-based method for Bayesian network structure learning based on correlated itemset mining techniques. The aim of this method is to identify and to represent conjunctions of Boolean factors implied in probabilistic dependence relationships, that may be ignored by constraint and scoring-based learning proposals when the pairwise dependencies are weak (e.g., noisy-...
Learning the structure of a Bayesian network from a data set is NP-hard. In this paper, we discuss a novel heuristic called polynomial max-min skeleton (PMMS) developed by Tsamardinos et al. in 2005. PMMS was proved by extensive empirical simulations to be an excellent trade-off between time and quality of reconstruction compared to all constraint based algorithms, especially for the smaller sample...
We describe the choice and assessment of neural network and statistical methods for data modelling, feature selection and forecasting. We deal in particular with how empirical environmental and Earth observation data can be used in conjunction with physical simulation models.
We describe the use of the wavelet transform for multivariate data analysis problems. In prediction, a multiscale transform of time-varying data can allow forecasts of each scale, followed by a combining of the individual forecasts. The use of a wavelet transform with noise modeling for point pattern clustering can lead to the result, which initially appears counter-intuitive, of clustering in constant...
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