the article suggests an algorithm for regular classifier ensemble methodology. the proposed methodology is based on possibilistic aggregation to classify samples. the argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. the optimization aims at learning backgrounds as solid clusters in subspaces of the high-dim...

We study luminosity and morphology segregation of cluster galaxies in an ensemble cluster built from 59 rich, nearby galaxy clusters observed in the ESO Nearby Cluster Survey (ENACS). The ensemble cluster contains 3056 member galaxies with positions, velocities and magnitudes ; 96% of these also have galaxy types. From positions and velocities we identify galaxies within substruc-tures, viz. as...

Feature selection is the process of identifying relevant features in the dataset and discarding everything else as irrelevant and redundant. Since feature selection reduces the dimensionality of the data, it enables the learning algorithms to operate more effectively and rapidly. In some cases, classification performance can be improved; in other instances, the obtained classifier is more compa...

This article presents a comprehensive study of different ensemble pruning techniques applied to a bagging ensemble composed of decision stumps. Six different ensemble pruning methods are tested. Four of these are greedy strategies based on first reordering the elements of the ensemble according to some rule that takes into account the complementarity of the predictors with respect to the classi...

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures....

In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In particular, a greedy sorting strategy is proposed to rearrange the order by which...

Ensemble learning constitutes one of the main directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. One technique, which proved to be effective for constructing an ensemble of diverse classifiers, is the use of feature subsets. Among different approaches to ensemble feature selection, genetic search was s...

Many nonparametric regression procedures are based on “subset selection”: they choose a subset of carriers from a large or even infinite set, and then determine the coefficients of the chosen carriers by least squares. Procedures which can be cast in this framework include Projection Pursuit, Turbo, Mars, and Matching Pursuit. Recently, considerable attention has been given to “ensemble estimat...