Multiclass maximum margin clustering via immune evolutionary algorithm for automatic diagnosis of electrocardiogram arrhythmias
Maximum margin clustering algorithm can obtain outstanding clustering performance by finding the maximum margin hyperplanes between clusters that can separate the data from different classes in an unsupervised way. However, it is only suitable for the clustering of small data set, since requires sol...
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| Published in | Applied mathematics and computation Vol. 227; pp. 428 - 436 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
15.01.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0096-3003 1873-5649 |
| DOI | 10.1016/j.amc.2013.11.028 |
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| Abstract | Maximum margin clustering algorithm can obtain outstanding clustering performance by finding the maximum margin hyperplanes between clusters that can separate the data from different classes in an unsupervised way. However, it is only suitable for the clustering of small data set, since requires solving non-convex integer problem, which is computationally expensive. In this paper, to further improve the clustering performance, a new multiclass clustering method based on maximum margin clustering algorithm and immune evolutionary algorithm (IEMMMC) is proposed for diagnosis of electrocardiogram (ECG) arrhythmias. Five types of ECG arrhythmias obtained from MIT-BIH database are analyzed in the experiment, including normal sinus rhythm (N), premature ventricular contraction (PVC), atrial premature contraction (APC), fusion of ventricular and normal beat (FVN), fusion of paced and normal beat (FPN). And three types of performance evaluation indicators are used to assess the effect of the IEMMMC method for ECG arrhythmias, such as sensitivity, specificity and accuracy. Compared with K-means, fuzzy c-means and LS-SVM algorithms, our method reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. |
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| AbstractList | Maximum margin clustering algorithm can obtain outstanding clustering performance by finding the maximum margin hyperplanes between clusters that can separate the data from different classes in an unsupervised way. However, it is only suitable for the clustering of small data set, since requires solving non-convex integer problem, which is computationally expensive. In this paper, to further improve the clustering performance, a new multiclass clustering method based on maximum margin clustering algorithm and immune evolutionary algorithm (IEMMMC) is proposed for diagnosis of electrocardiogram (ECG) arrhythmias. Five types of ECG arrhythmias obtained from MIT-BIH database are analyzed in the experiment, including normal sinus rhythm (N), premature ventricular contraction (PVC), atrial premature contraction (APC), fusion of ventricular and normal beat (FVN), fusion of paced and normal beat (FPN). And three types of performance evaluation indicators are used to assess the effect of the IEMMMC method for ECG arrhythmias, such as sensitivity, specificity and accuracy. Compared with K-means, fuzzy c-means and LS-SVM algorithms, our method reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. |
| Author | Ding, Yongsheng Hao, Kuangrong Zhu, Bohui |
| Author_xml | – sequence: 1 givenname: Bohui surname: Zhu fullname: Zhu, Bohui organization: College of Information Sciences and Technology, Donghua University, Shanghai 201620, PR China – sequence: 2 givenname: Yongsheng surname: Ding fullname: Ding, Yongsheng email: ysding@dhu.edu.cn organization: College of Information Sciences and Technology, Donghua University, Shanghai 201620, PR China – sequence: 3 givenname: Kuangrong surname: Hao fullname: Hao, Kuangrong organization: College of Information Sciences and Technology, Donghua University, Shanghai 201620, PR China |
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| CitedBy_id | crossref_primary_10_1177_1460458217717636 crossref_primary_10_1016_j_engappai_2024_108457 crossref_primary_10_1016_j_knosys_2014_12_001 crossref_primary_10_1016_j_knosys_2016_01_037 crossref_primary_10_1007_s13246_018_0676_1 crossref_primary_10_12677_JISP_2018_74024 crossref_primary_10_4018_JDM_2018070102 crossref_primary_10_1109_ACCESS_2019_2916724 crossref_primary_10_1007_s00521_023_09388_x crossref_primary_10_1155_2022_7276028 crossref_primary_10_3233_IFS_151910 crossref_primary_10_1016_j_ijmedinf_2016_09_005 crossref_primary_10_1109_TCYB_2018_2866527 |
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| SubjectTerms | Algorithms Arrhythmia Clustering Computation Diagnosis ECG arrhythmias Evolutionary algorithms Immune evolutionary algorithm Maximum margin clustering Multiclass clustering Searching |
| Title | Multiclass maximum margin clustering via immune evolutionary algorithm for automatic diagnosis of electrocardiogram arrhythmias |
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