Learn More
Ensemble methods based on bias–variance analysis Theses Series Abstract Ensembles of classifiers represent one of the main research directions in machine learning. Two main theories are invoked to explain the success of ensemble methods. The first one consider the ensembles in the framework of large margin classifiers, showing that ensembles enlarge the(More)
Bias-variance analysis provides a tool to study learning algorithms and can be used to properly design ensemble methods well tuned to the properties of a specific base learner. Indeed the effectiveness of ensemble methods critically depends on accuracy, diversity and learning characteristics of base learners. We present an extended experimental analysis of(More)
Recently, bias-variance decomposition of error has been used as a tool to study the behavior of learning algorithms and to develop new ensemble methods well suited to the bias-variance characteristics of base learners. We propose methods and procedures, based on Domingo's unified bias-variance theory, to evaluate and quantitatively measure the bias-variance(More)
Expression-based classification of tumors requires stable, reliable and variance reduction methods, as DNA microarray data are characterized by low size, high di-mensionality, noise and large biological variability. In order to address the variance and curse of dimensionality problems arising from this difficult task, we propose to apply bagged ensembles of(More)
UNLABELLED The R package mosclust (model order selection for clustering problems) implements algorithms based on the concept of stability for discovering significant structures in bio-molecular data. The software library provides stability indices obtained through different data perturbations methods (resampling, random projections, noise injection), as(More)
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for(More)
The ranking and prediction of novel therapeutic categories for existing drugs (drug repositioning) is a challenging computational problem involving the analysis of complex chemical and biological networks. In this context we propose a novel semi-supervised learning problem: ranking drugs in integrated biochemical networks according to specific DrugBank(More)
Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy(More)