NDLI: Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis

Content Provider	Springer Nature Link
Author	Jadhav, Shivajirao Nalbalwar, Sanjay Ghatol, Ashok
Copyright Year	2013
Abstract	Various methods for ensembles selection and classifier combination have been designed to optimize the performance of ensembles of classifiers. However, use of large number of features in training data can affect the classification performance of machine learning algorithms. The objective of this paper is to represent a novel feature elimination (FE) based ensembles learning method which is an extension to an existing machine learning environment. Here the standard 12 lead ECG signal recordings data have been used in order to diagnose arrhythmia by classifying it into normal and abnormal subjects. The advantage of the proposed approach is that it reduces the size of feature space by way of using various feature elimination methods. The decisions obtained from these methods have been coalesced to form a fused data. Thus the idea behind this work is to discover a reduced feature space so that a classifier built using this tiny data set would perform no worse than a classifier built from the original data set. Random subspace based ensembles classifier is used with PART tree as base classifier. The proposed approach has been implemented and evaluated on the UCI ECG signal data. Here, the classification performance has been evaluated using measures such as mean absolute error, root mean squared error, relative absolute error, F-measure, classification accuracy, receiver operating characteristics and area under curve. In this way, the proposed novel approach has provided an attractive performance in terms of overall classification accuracy of 91.11 % on unseen test data set. From this work, it is shown that this approach performs well on the ensembles size of 15 and 20.
Starting Page	579
Ending Page	587
Page Count	9
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	18
Issue Number	3
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-08-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data fusion ECG arrhythmia Ensembles learning Feature elimination Random subspace method Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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