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Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis

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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.

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Acknowledgments

The authors would like to put on record their heart-felt thanks to the University Grants Commission (UGC), New Delhi and authority of Dr. Babasaheb Ambedkar Technological University, Lonere for providing ‘Teacher Fellowship Award’ to the corresponding author for his Ph.D. study. The corresponding author owe a sense of gratitude to Dr. B. B. Singh who helped in proof reading of this paper.

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Authors and Affiliations

  1. Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad District, 402 103, MS, India

    Shivajirao Jadhav & Sanjay Nalbalwar

  2. K. J. Educational Institutes Pisoli, Kondhwa-Saswad Road, Near Bopdeo Ghat, Taluka Haveli, Pune, 411 048, MS, India

    Ashok Ghatol

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  1. Shivajirao Jadhav
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  2. Sanjay Nalbalwar
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Correspondence to Shivajirao Jadhav.

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Communicated by G. Acampora.

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Jadhav, S., Nalbalwar, S. & Ghatol, A. Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis. Soft Comput 18, 579–587 (2014). https://doi.org/10.1007/s00500-013-1079-6

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