Ensemble learning (process of combining multiple models into a single decision) is an effective tool for improving the classification performance of inductive models. While ideal for domains like bioinformatics with many challenging datasets, many ensemble methods, such as Bagging and Boosting, do not take into account the high-dimensionality (large number of features per instance) that is comm...

Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) ensemble, the number of classifiers needed i...

Markov Blankets discovery algorithms are important for learning a Bayesian network structure. We present an argument that tree ensemble masking measures can provide an approximate Markov blanket. Then an ensemble feature selection method is used to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). We compare our algorithm in the causal stru...

Ensemble Feature Selection for Multi-Stream Automatic Speech Recognition

A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its red...

Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ensembles have been examined in the literature, e.g., random feature selection, weak clusterers, random projections, etc. Typically, one heuristic is picked at a time to construct the ensemble. To increase diversity of the ense...

Several dimension reduction techniques for microarray data have been developed over the years to get differentially expressed genes. However, a very little consensus in their resultant feature subsets for a particular dataset has been noticed. Therefore, to address the aforesaid issue an ensemble of feature selection technique is proposed in this paper. The ensemble is a combination of well bal...