Increasing Weak Classifier Diversity by Omics Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00234034" target="_blank" >RIV/68407700:21230/15:00234034 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Increasing Weak Classifier Diversity by Omics Networks

Popis výsledku v původním jazyce

The common problems in machine learning from omics data are the scarcity of samples, the high number of features and their complex interaction structure. The models built solely from measured data often suffer from overfitting. One of possible methods dealing with overfitting is to use prior knowledge for regularization. This work analyzes contribution of feature interaction networks in regularization of ensemble classifiers representing another approach to overfitting reduction. We study how utilization of feature interaction networks influences diversity of weak classifiers and thus accuracy of the resulting ensemble model. The network and its random walks are used to control the feature randomization during construction of weak classifiers, which makes them more diverse than in the well-known random forest. We experiment with different types of weak classifiers (trees, logistic regression, naive Bayes) and different random walk lengths and demonstrate that diversity of weak classifiers grows with increasing network locality of weak classifiers.

Název v anglickém jazyce

Increasing Weak Classifier Diversity by Omics Networks

Popis výsledku anglicky

The common problems in machine learning from omics data are the scarcity of samples, the high number of features and their complex interaction structure. The models built solely from measured data often suffer from overfitting. One of possible methods dealing with overfitting is to use prior knowledge for regularization. This work analyzes contribution of feature interaction networks in regularization of ensemble classifiers representing another approach to overfitting reduction. We study how utilization of feature interaction networks influences diversity of weak classifiers and thus accuracy of the resulting ensemble model. The network and its random walks are used to control the feature randomization during construction of weak classifiers, which makes them more diverse than in the well-known random forest. We experiment with different types of weak classifiers (trees, logistic regression, naive Bayes) and different random walk lengths and demonstrate that diversity of weak classifiers grows with increasing network locality of weak classifiers.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2nd Workshop on Machine Learning in Life Sciences

ISBN

978-83-943803-0-4

ISSN

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e-ISSN

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Počet stran výsledku

13

Strana od-do

16-28

Název nakladatele

ENGINE - European Research Centre of Network Inteligence and Innovation, Wroclaw University of Technology

Místo vydání

Wroclaw

Místo konání akce

Porto

Datum konání akce

7. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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