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Title:Comprehensive decision tree models in bioinformatics
Authors:ID Štiglic, Gregor (Author)
ID Kocbek, Simon (Author)
ID Pernek, Igor (Author)
ID Kokol, Peter (Author)
Files:.pdf PLoS_ONE_2012_Stiglic_et_al._Comprehensive_Decision_Tree_Models_in_Bioinformatics.PDF (524,39 KB)
MD5: 178714A3D213A7249931984D69E5E830
PID: 20.500.12556/dkum/a5749aeb-74f9-42e8-9427-a9b6284d08ca
URL http://dx.plos.org/10.1371/journal.pone.0033812
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FZV - Faculty of Health Sciences
Abstract:Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did notexpected significant differences in classification performance, the resultsdemonstrate a significant increase of accuracy in less complex visuallytuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumptionthat the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes anda high number of possibly redundant attributes that are very common in bioinformatics.
Keywords:decision tree models, machine learning technique, visual tuning, bioinformatics
Publication status:Published
Publication version:Version of Record
Year of publishing:2012
Number of pages:str. 1-13
Numbering:Letn. 7, št. 3
PID:20.500.12556/DKUM-30867 New window
ISSN on article:1932-6203
COBISS.SI-ID:1788068 New window
DOI:10.1371/journal.pone.0033812 New window
Publication date in DKUM:05.06.2012
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Record is a part of a journal

Title:PloS one
Publisher:Public Library of Science
COBISS.SI-ID:2005896 New window


License:CC BY 4.0, Creative Commons Attribution 4.0 International
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:05.06.2012

Secondary language

Keywords:drevo odločanja, strojno učenje, vizualno uravnavanje, bioinformatika


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