Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. ...

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of the proposed measure are selected to participate in clustering ensemble. To combine the chosen clusters, some methods are employed as aggregators. Employing this new cluster validation criterion, the ...

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a coassociation based consensus function is applied. Since the Evidence Accumulation Clustering meth...

Unlike structures determined by X-ray crystallography, which are deposited in the Brookhaven Protein Data Bank (Abola et al., 1987) as a single structure, each NMR-derived structure is often deposited as an ensemble containing many structures, each consistent with the restraint set used. However, there is often a need to select a single 'representative' structure, or a 'representative' subset o...

Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. They use different directions for searching this space and different measures for evaluating the available a...

The majority of ensemble creation algorithms use the full set of available features for its task. Feature selection for ensemble creation has not been carried out except for some work on random feature selection. In this paper we focus our attention on genetic based feature selection for ensemble creation. Our approach uses a genetic algorithm to search over the entire feature space. Subsets of...

An extensive amount of work has been done in data clustering research under the unsupervised learning technique in Data Mining during the past two decades. Moreover, several approaches and methods have been emerged focusing on clustering diverse data types, features of cluster models and similarity rates of clusters. However, none of the single clustering algorithm exemplifies its best nature i...

Web content mining retrieves the information from web in more structured forms. The page rank plays an essential part process. Whenever user searches for any on web, relevant is shown at top of list through ranking. Many existing ranking algorithms were developed and failed to pages accurate manner minimum time feeding. In direction address above mentioned issues, Lancaster Stem Sammon Projecti...

This paper studies the asymptotic relationship between Bayesian model averaging and postselection frequentist predictors in both nested and nonnested models. We derive conditions under which their difference is of a smaller order of magnitude than the inverse of the square root of the sample size in large samples. This result depends crucially on the relation between posterior odds and frequent...

Increasing access to large-scale, high-dimensional and non-stationary streams in many real applications has made it necessary to design new dynamic classification algorithms. Most existing approaches for the textual stream classification are able to train the model relying on labeled data. However, only a limited number of instances can be labeled in a real streaming environment since large-sca...