Active Learning Methods for Electrocardiographic Signal Classification
IEEE Transactions on Information Technology in BiomedicinePublished 1 November 2010
Edoardo Pasolli, Farid Melgani
Citations95
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TL;DR
The obtained results show that the proposed strategies exhibit a promising capability to select samples that are significant for the classification process, i.e., to boost the accuracy of the Classification process while minimizing the number of involved labeled samples.
Abstract
In this paper, we present three active learning strategies for the classification of electrocardiographic (ECG) signals. Starting from a small and suboptimal training set, these learning strategies select additional beat samples from a large set of unlabeled data. These samples are labeled manually, and then added to the training set. The entire procedure is iterated until the construction of a final training set representative of the considered classification problem. The proposed methods are based on support vector machine classification and on the: 1) margin sampling; 2) posterior probability; and 3) query by committee principles, respectively. To illustrate their performance, we conducted an experimental study based on both simulated data and real ECG signals from the MIT-BIH arrhythmia database. In general, the obtained results show that the proposed strategies exhibit a promising capability to select samples that are significant for the classification process, i.e., to boost the accuracy of the classification process while minimizing the number of involved labeled samples.
Keywords
Computer ScienceMedicineEngineering
TechnometricsStatistical Learning Theory
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