Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Diagnosis of valvular heart disease through neural networks ensembles
Computer Methods and Programs in Biomedicine
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Computational Biology and Chemistry
Computers in Biology and Medicine
Evaluating data mining algorithms using molecular dynamics trajectories
International Journal of Data Mining and Bioinformatics
Indirect immunofluorescence image classification using texture descriptors
Expert Systems with Applications: An International Journal
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In this work, we investigate the use of ensemble learning for improving classifiers which is one of the important directions in the current research of machine learning, in which bagging, boosting and random subspace are three powerful and popular representatives. Researchers have so far shown the efficacies of ensemble methods in many practical classification problems. However, for valvular heart disease detection, there are almost no studies investigating their feasibilities. Thus, in this study, we evaluate the performance of three popular ensemble methods for the diagnosis of the valvular heart disorders. To evaluate the performance of investigated ensemble methodology, a comparative study is realized by using a data set containing 215 samples. Moreover, to achieve a comprehensive comparison, we consider the previous results reported by earlier methods (Comak, Arslan, & Turkoglu, 2007; Sengur, 2008a,b; Sengur & Turkoglu, 2008; Turkoglu, Arslan, & Ilkay, 2002, 2003; Uguz, Arslan, & Turkoglu, 2007). Experimental results suggest the feasibilities of ensemble classification methods, and we also derive some valuable conclusions on the performance of ensemble methods for valvular heart disease detection.