Increasing Weak Classifier Diversity by Omics Networks
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Increasing Weak Classifier Diversity by Omics Networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
13
Pages from-to
16-28
Publisher name
ENGINE - European Research Centre of Network Inteligence and Innovation, Wroclaw University of Technology
Place of publication
Wroclaw
Event location
Porto
Event date
Sep 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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