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Pregled bibliografske jedinice broj: 909801

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


Vidulin, Vedrana; Brbić, Maria; Supek, Fran; Šmuc, Tomislav
Evaluation of fusion approaches in large-scale bio-annotation setting // 4th Workshop on Machine Learning in Life Science / Ksieniewicz, Pawel (ur.).
Riva del Garda, Italy, 2016. str. 37-51 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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
4th Workshop on Machine Learning in Life Science / Ksieniewicz, Pawel - , 2016, 37-51

Skup
4th Workshop on Machine Learning in Life Science

Mjesto i datum
Riva del Garda, Italy, 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


Ustanove
Institut "Ruđer Bošković", Zagreb