DocumentCode :
639758
Title :
Discriminating proteins using a novel ensemble algorithm
Author :
Nikookar, Elham ; Badie, Kambiz ; Sadeghi, Mohammadreza ; Naderi, Elahe
Author_Institution :
Dept. of algorithm & Comput., Univ. of Tehran, Tehran, Iran
fYear :
2013
fDate :
28-30 May 2013
Firstpage :
318
Lastpage :
322
Abstract :
Decisions of multiple hypotheses are combined in ensemble learning to produce more accurate and less risky results. In this article, we present a novel ensemble machine learning approach for the development of robust thermo stable protein discrimination. But unlike widely used ensemble approaches in which bootstrapped training data are used, we keep the original data unchanged. Instead, we build an ensemble of base classifiers that each of them uses a division of features (called feature group) to predict the class label of each sample. Then, we try to learn the base classifiers´ outputs (behavior) for different samples. By testing the proposed method on a well-known dataset, we show that our ensemble method is comparable in precision, recall and f-measure to the state of the art classifiers.
Keywords :
biology computing; learning (artificial intelligence); pattern classification; proteins; base classifier ensemble; bootstrapped training data; ensemble learning algorithm; f-measure; machine learning approach; multiple hypotheses decisions; robust thermostable protein discrimination; Amino acids; Bioinformatics; Boosting; Decision trees; Feature extraction; Proteins; Support vector machines; Base classifier; Bioinformatics; Ensemble; Learning; Protein Discrimination;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Knowledge Technology (IKT), 2013 5th Conference on
Conference_Location :
Shiraz
Print_ISBN :
978-1-4673-6489-8
Type :
conf
DOI :
10.1109/IKT.2013.6620086
Filename :
6620086
Link To Document :
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