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Phenotype prediction with semi-supervised learning (CROSBI ID 655809)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Levatić, Jurica ; Brbić, Maria ; Stepišnik Perdih, Tomaž ; Kocev, Dragi ; Vidulin, Vedrana ; Šmuc, Tomislav ; Supek, Fran ; Džeroski, Sašo Phenotype prediction with semi-supervised learning // New frontiers in mining complex patterns NFMCP 2017, Lecture Notes in Computer Science. 2017. str. 1-11

Podaci o odgovornosti

Levatić, Jurica ; Brbić, Maria ; Stepišnik Perdih, Tomaž ; Kocev, Dragi ; Vidulin, Vedrana ; Šmuc, Tomislav ; Supek, Fran ; Džeroski, Sašo

engleski

Phenotype prediction with semi-supervised learning

In this work, we address the task of phenotypic traits prediction using methods for semi- supervised learning. More specifically, we propose to use supervised and semi-supervised classification trees as well as supervised and semi-supervised random forests of classification trees. We consider 114 datasets for different phenotypic traits referring to 997 microbial species. These datasets present a challenge for the existing machine learning methods: they are not labelled/annotated entirely and their distribution is typically imbalanced. We investigate whether approaching the task of phenotype prediction as a semi- supervised learning task can yield improved predictive performance. The result suggest that the semi-supervised methodology considered here is helpful for phenotype prediction for which the amount of labeled data ranges from 20 to 40%. Furthermore, the semi-supervised classification trees exhibit good predictive performance for datasets where the presence of a given trait is not extremely imbalanced (i.e., less than 6%).

semi-supervised learning ; phenotype ; decision trees ; predictive clustering trees ; random forests ; binary classification

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Podaci o prilogu

1-11.

2017.

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objavljeno

Podaci o matičnoj publikaciji

New frontiers in mining complex patterns NFMCP 2017, Lecture Notes in Computer Science

Podaci o skupu

New frontiers in mining complex patterns: Sixth edition of the International Workshop NFMCP 2017 in conjunction with ECML-PKDD 2017

predavanje

18.09.2017-22.09.2017

Skopje, Sjeverna Makedonija

Povezanost rada

Računarstvo

Poveznice