engleski

Evaluation of fusion approaches in large-scale bio-annotation setting

In this work we compare different information fusion approaches in the context of large-scale multi-label classification problems, typical today in bio-domains: early fusion, late fusion and hybrid fusion approach. The experiments are performed on two novel large-scale classification datasets for gene function prediction and prokaryotic phenotype prediction. Both datasets are based on descriptors coming from a number of different representations of biological entities. The results reveal that the fusion approaches exploiting complementarity are best suited for difficult annotation problems featured in complex datasets from bio-domains for which individual classifiers perform well only locally.

Information fusion ; Ensemble classifier design ; Diversity ; Genomics

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