Giorgio Valentini

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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)
Gene function prediction is a complex computational problem, characterized by several items: the number of functional classes is large, and a gene may belong to multiple classes; functional classes are structured according to a hierarchy; classes are usually unbalanced, with more negative than positive examples; class labels can be uncertain and the(More)
Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In(More)
OBJECTIVE Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The main goal of this work consists in the(More)
Gene function prediction is a complex multilabel classification problem with several distinctive features: the hierarchical relationships between functional classes, the presence of multiple sources of biomolecular data, the unbalance between positive and negative examples for each class, the complexity of the whole-ontology and genome-wide dimensions.(More)
Expression-based classification of tumors requires stable, reliable and variance reduction methods, as DNA microarray data are characterized by low size, high dimensionality, 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)
Hierarchical classification problems gained increasing attention within the machine learning community, and several methods for hierarchically structured taxonomies have been recently proposed, with applications ranging from classification of web documents to bioinformatics. In this paper we propose a novel ensemble algorithm for multilabel, multi-path,(More)
In this work we propose new ensemble methods for the hierarchical classification of gene functions. Our methods exploit the hierarchical relationships between the classes in different ways: each ensemble node is trained “locally”, according to its position in the hierarchy; moreover, in the evaluation phase the set of predicted annotations is built so to(More)
Multiple classifier systems are widely used in security<lb>applications like biometric personal authentication, spam filtering,<lb>and intrusion detection in computer networks. Several works exper-<lb>imentally showed their effectiveness in these tasks. However, their<lb>use in such applications is motivated only by intuitive and qualitative<lb>arguments.(More)