Improved Classification for Problem Involving Overlapping Patterns
Summary :
The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.11 pp.1787-1795
- Publication Date
- 2007/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.11.1787
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
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Yaohua TANG, Jinghuai GAO, "Improved Classification for Problem Involving Overlapping Patterns" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 11, pp. 1787-1795, November 2007, doi: 10.1093/ietisy/e90-d.11.1787.
Abstract: The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.11.1787/_p
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@ARTICLE{e90-d_11_1787,
author={Yaohua TANG, Jinghuai GAO, },
journal={IEICE TRANSACTIONS on Information},
title={Improved Classification for Problem Involving Overlapping Patterns},
year={2007},
volume={E90-D},
number={11},
pages={1787-1795},
abstract={The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.},
keywords={},
doi={10.1093/ietisy/e90-d.11.1787},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Improved Classification for Problem Involving Overlapping Patterns
T2 - IEICE TRANSACTIONS on Information
SP - 1787
EP - 1795
AU - Yaohua TANG
AU - Jinghuai GAO
PY - 2007
DO - 10.1093/ietisy/e90-d.11.1787
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2007
AB - The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.
ER -
English
