The ever increasing amount of biomolecular data available in public domain databases for a broad range of organisms coupled with recent advances in machine learning research has stimulated interest in computational approaches on gene function prediction. In this context data integration from heterogeneous biomolecular data sources plays a key role. In this contribution we test the performance o...

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 this paper we present a brief overview o...

Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the training process. However, the reliance on labeled data precludes the applic...

Semi-supervised learning and ensemble learning are two important machine learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. Although both paradigms have achieved great success during the past decade, they were almost developed separately. In this paper, we advocat...

In the last decades ensemble learning has established itself as a valuable strategy within the computational intelligence modeling and machine learning community. Ensemble learning is a paradigm where multiple models combine in some way their decisions, or their learning algorithms, or different data to improve the prediction performance. Ensemble learning aims at improving the generalization a...

Diversity among individual classifiers is recognized to play a key role in ensemble, however, few theoretical properties are known for classification. In this paper, by focusing on the popular ensemble pruning setting (i.e., combining classifier by voting and measuring diversity in pairwise manner), we present a theoretical study on the effect of diversity on the generalization performance of v...

Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named Sealed is p...

INTRODUCTION Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be a...

The relationship between ensemble classifier performance and the diversity of the predictions made by ensemble base classifiers is explored in the context of heterogeneous ensemble classifiers. Specifically, numerical studies indicate that heterogeneous ensembles can be generated from base classifiers of homogeneous ensemble classifiers that are both significantly more accurate and diverse than...