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 | Tagi: 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 | Tagi: 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. |
2011 |
Luckner, Marcin Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. Links | BibTeX | Tagi: Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One @inproceedings{Luckner2011, title = {Multiclass SVM classification using graphs calibrated by similarity between classes}, author = {Marcin Luckner}, doi = {10.1007/978-3-642-23866-6_46}, issn = {03029743}, year = {2011}, date = {2011-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {6884 LNAI}, number = {PART 4}, pages = {435--444}, keywords = {Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. Abstract | Links | BibTeX | Tagi: Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines @inproceedings{Luckner2011a, title = {Reducing Number of Classifiers in DAGSVM Based on Class Similarity}, author = {Marcin Luckner}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-24085-0_53}, doi = {10.1007/978-3-642-24085-0_53}, year = {2011}, date = {2011-01-01}, booktitle = {Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science}, pages = {514--523}, publisher = {Springer Berlin Heidelberg}, abstract = {Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly.}, keywords = {Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly. |
Luckner, Marcin Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. Links | BibTeX | Tagi: Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One @inproceedings{Luckner2011c, title = {Multiclass SVM classification using graphs calibrated by similarity between classes}, author = {Marcin Luckner}, doi = {10.1007/978-3-642-23866-6_46}, issn = {03029743}, year = {2011}, date = {2011-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {6884 LNAI}, number = {PART 4}, pages = {435--444}, keywords = {Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. Abstract | Links | BibTeX | Tagi: Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines @inproceedings{Luckner2011ab, title = {Reducing Number of Classifiers in DAGSVM Based on Class Similarity}, author = {Marcin Luckner}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-24085-0_53}, doi = {10.1007/978-3-642-24085-0_53}, year = {2011}, date = {2011-01-01}, booktitle = {Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science}, pages = {514--523}, publisher = {Springer Berlin Heidelberg}, abstract = {Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly.}, keywords = {Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly. |
Publikacje
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. |
2011 |
Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. |
Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. |
Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. |
Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. |