Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/629367
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dc.contributor.authorCheng Zhouen_US
dc.contributor.authorBoris Culeen_US
dc.contributor.authorBart Goethalsen_US
dc.date.accessioned2020-05-20T09:43:26Z-
dc.date.available2020-05-20T09:43:26Z-
dc.date.issued2016en_US
dc.identifier.issn1041-4347en_US
dc.identifier.other10.1109/TKDE.2015.2510010en_US
dc.identifier.urihttp://localhost/handle/Hannan/156339en_US
dc.identifier.urihttp://localhost/handle/Hannan/629367-
dc.descriptionen_US
dc.description.abstractSequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.en_US
dc.publisherIEEEen_US
dc.relation.haspart7360207.pdfen_US
dc.subjectclassification rules|interesting patterns|feature vectors|sequence classificationen_US
dc.titlePattern Based Sequence Classificationen_US
dc.typeArticleen_US
dc.journal.volume28en_US
dc.journal.issue5en_US
dc.journal.titleIEEE Transactions on Knowledge and Data Engineeringen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheng Zhouen_US
dc.contributor.authorBoris Culeen_US
dc.contributor.authorBart Goethalsen_US
dc.date.accessioned2020-05-20T09:43:26Z-
dc.date.available2020-05-20T09:43:26Z-
dc.date.issued2016en_US
dc.identifier.issn1041-4347en_US
dc.identifier.other10.1109/TKDE.2015.2510010en_US
dc.identifier.urihttp://localhost/handle/Hannan/156339en_US
dc.identifier.urihttp://localhost/handle/Hannan/629367-
dc.descriptionen_US
dc.description.abstractSequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.en_US
dc.publisherIEEEen_US
dc.relation.haspart7360207.pdfen_US
dc.subjectclassification rules|interesting patterns|feature vectors|sequence classificationen_US
dc.titlePattern Based Sequence Classificationen_US
dc.typeArticleen_US
dc.journal.volume28en_US
dc.journal.issue5en_US
dc.journal.titleIEEE Transactions on Knowledge and Data Engineeringen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360207.pdf668.1 kBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheng Zhouen_US
dc.contributor.authorBoris Culeen_US
dc.contributor.authorBart Goethalsen_US
dc.date.accessioned2020-05-20T09:43:26Z-
dc.date.available2020-05-20T09:43:26Z-
dc.date.issued2016en_US
dc.identifier.issn1041-4347en_US
dc.identifier.other10.1109/TKDE.2015.2510010en_US
dc.identifier.urihttp://localhost/handle/Hannan/156339en_US
dc.identifier.urihttp://localhost/handle/Hannan/629367-
dc.descriptionen_US
dc.description.abstractSequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.en_US
dc.publisherIEEEen_US
dc.relation.haspart7360207.pdfen_US
dc.subjectclassification rules|interesting patterns|feature vectors|sequence classificationen_US
dc.titlePattern Based Sequence Classificationen_US
dc.typeArticleen_US
dc.journal.volume28en_US
dc.journal.issue5en_US
dc.journal.titleIEEE Transactions on Knowledge and Data Engineeringen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360207.pdf668.1 kBAdobe PDFThumbnail
Preview File