Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TITB.2010.2048922 | IEEE Transactions on Information Technology in Biomedicine |
Keywords | Field | DocType |
classification process,real ecg signal,final training set representative,simulated data,active learning strategy,suboptimal training set,active learning method,support vector machine classification,training set,large set,electrocardiographic signal classification,considered classification problem,support vector machine,accuracy,posterior probability,active learning,classification algorithms,support vector machines | Data mining,One-class classification,Active learning,Pattern recognition,Computer science,Support vector machine,Posterior probability,Sampling (statistics),Artificial intelligence,Statistical classification,Iterated function,Principal component analysis | Journal |
Volume | Issue | ISSN |
14 | 6 | 1558-0032 |
Citations | PageRank | References |
26 | 1.02 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edoardo Pasolli | 1 | 285 | 17.04 |
| Farid Melgani | 2 | 1100 | 80.98 |