2014 |
Homenda, Wladyslaw; Luckner, Marcin Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. Abstract | Links | BibTeX | Tags: Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition @inproceedings{Homenda2014a, title = {Pattern recognition with rejection: Application to handwritten digits}, author = {Wladyslaw Homenda and Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7077288}, doi = {10.1109/WICT.2014.7077288}, isbn = {978-1-4799-8115-1}, year = {2014}, date = {2014-12-01}, booktitle = {2014 4th World Congress on Information and Communication Technologies (WICT 2014)}, pages = {326--331}, publisher = {IEEE}, abstract = {The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study.}, keywords = {Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition}, pubstate = {published}, tppubtype = {inproceedings} } The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study. |
Homenda, Wladyslaw; Luckner, Marcin Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. Abstract | Links | BibTeX | Tags: Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition @inproceedings{Homenda2014ab, title = {Pattern recognition with rejection: Application to handwritten digits}, author = {Wladyslaw Homenda and Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7077288}, doi = {10.1109/WICT.2014.7077288}, isbn = {978-1-4799-8115-1}, year = {2014}, date = {2014-12-01}, booktitle = {2014 4th World Congress on Information and Communication Technologies (WICT 2014)}, pages = {326--331}, publisher = {IEEE}, abstract = {The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study.}, keywords = {Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition}, pubstate = {published}, tppubtype = {inproceedings} } The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study. |
2013 |
Luckner, Marcin; Szyszko, Krzysztof RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. Abstract | Links | BibTeX | Tags: Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository @inproceedings{Luckner2013a, title = {RBF ensemble based on reduction of DAG structure}, author = {Marcin Luckner and Krzysztof Szyszko}, url = {https://fedcsis.org/proceedings/2013/pliks/334.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the 2013 Federated Conference on Computer Science and Information Systems}, pages = {99--105}, publisher = {IEEE}, address = {Kraków}, abstract = {Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly.}, keywords = {Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository}, pubstate = {published}, tppubtype = {inproceedings} } Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly. |
Luckner, Marcin; Szyszko, Krzysztof RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. Abstract | Links | BibTeX | Tags: Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository @inproceedings{Luckner2013ab, title = {RBF ensemble based on reduction of DAG structure}, author = {Marcin Luckner and Krzysztof Szyszko}, url = {https://fedcsis.org/proceedings/2013/pliks/334.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the 2013 Federated Conference on Computer Science and Information Systems}, pages = {99--105}, publisher = {IEEE}, address = {Kraków}, abstract = {Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly.}, keywords = {Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository}, pubstate = {published}, tppubtype = {inproceedings} } Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly. |
Publications
2014 |
Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. |
Pattern recognition with rejection: Application to handwritten digits Inproceedings 2014 4th World Congress on Information and Communication Technologies (WICT 2014), pp. 326–331, IEEE, 2014, ISBN: 978-1-4799-8115-1. |
2013 |
RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. |
RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. |