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

Autori
Vidulin, Vedrana ; Brbić, Maria ; Supek, Fran ; Šmuc, Tomislav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 4th Workshop on Machine Learning in Life Sciences / Ksieniewicz, Pawel - Wroclaw : European Research Centre of Network Intelligence for Innovation Enhancement, 2016, 37-51

ISBN
978-83-943803-1-1

Skup
4th Workshop on Machine Learning in Life Science

Mjesto i datum
Riva del Garda, Italija, 23.09.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Information fusion ; Ensemble classifier design ; Diversity ; Genomics

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA