A very promising tool for data mining and bioinformatics is ensemble gene (feature) selection. Ensemble feature selection is the process of performing multiple runs of feature selection and then aggregating the results into a final ranked list. However, a central question of ensemble feature selection is how to aggregate the individual results into a single ranked feature list. There are a numb...

Feature selection for ensembles has shown to be an effective strategy for ensemble creation. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The first level performs feature selection in order to generate a set of good classifiers while the second one combines them to provide a set of powerful ensembles. The proposed met...

One of the major challenges in bioinformatics is selecting the appropriate genes for a given problem, and moreover, choosing the best gene selection technique for this task. Many such techniques have been developed, each with its own characteristics and complexities. Recently, some works have addressed this by introducing ensemble gene selection, which is the process of performing multiple runs...

in this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. then, for optimizing the initial feature vector, a genetic algorithm is proposed. some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and k-nearest neig...

Abstract To mitigate the curse of dimensionality in high-dimensional datasets, feature selection has become a crucial step most data mining applications. However, no method consistently delivers best performance across different domains. For this reason and order to improve stability process, ensemble frameworks have increasingly popular. While many examined construction techniques under variou...

It is known that an ensemble of classifiers can outperform a single best classifier if classifiers in the ensemble are sufficiently diverse (i.e., their errors are as much uncorrelated as possible) and accurate. We study ensembles of nearest neighbours for cancer classification based on gene expression data. Such ensembles have been rarely used, because the traditional ensemble methods such as ...