Naslov
Decision tree ensembles in biomedical time-series classification

Autori
Jović, Alan ; Brkić, Karla ; Bogunović, Nikola

Izvornik
Lecture Notes in Computer Science (0302-9743) 7476 (2012); 408-417

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
decision tree ; classifier ensembles ; support vector machines ; biomedical time-series analysis

Sažetak
There are numerous classification methods developed in the field of machine learning. Some of these methods, such as artificial neural networks and support vector machines, are used extensively in biomedical time-series classification. Other methods have been used less often for no apparent reason. The aim of this work is to examine the applicability of decision tree ensembles as strong and practical classification algorithms in biomedical domain. We consider four common decision tree ensembles: AdaBoost.M1+C4.5, Multi-Boost+C4.5, random forest, and rotation forest. The decision tree ensembles are compared with SMO-based support vector machines classifiers (linear, squared polynomial, and radial kernel) on three distinct biomedical time-series datasets. For evaluation purposes, 10x10-fold cross-validation is used and the classifiers are measured in terms of sensitivity, specificity, and speed of model construction. The classifiers are compared in terms of statistically significant wins-losses- ties on the three datasets. We show that the overall results favor decision tree ensembles over SMO-based support vector machines. Preliminary results suggest that AdaBoost.M1 and MultiBoost are the best of the examined classifiers, with no statistically significant difference between them. These results should encourage the use of decision tree ensembles in biomedical time-series datasets where optimal model accuracy is sought.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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