Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/629367
Title: Pattern Based Sequence Classification
Authors: Cheng Zhou;Boris Cule;Bart Goethals
subject: classification rules|interesting patterns|feature vectors|sequence classification
Year: 2016
Publisher: IEEE
Abstract: Sequence 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.
Description: 
URI: http://localhost/handle/Hannan/156339
http://localhost/handle/Hannan/629367
ISSN: 1041-4347
volume: 28
issue: 5
Appears in Collections:2016

Files in This Item:
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Title: Pattern Based Sequence Classification
Authors: Cheng Zhou;Boris Cule;Bart Goethals
subject: classification rules|interesting patterns|feature vectors|sequence classification
Year: 2016
Publisher: IEEE
Abstract: Sequence 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.
Description: 
URI: http://localhost/handle/Hannan/156339
http://localhost/handle/Hannan/629367
ISSN: 1041-4347
volume: 28
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360207.pdf668.1 kBAdobe PDFThumbnail
Preview File
Title: Pattern Based Sequence Classification
Authors: Cheng Zhou;Boris Cule;Bart Goethals
subject: classification rules|interesting patterns|feature vectors|sequence classification
Year: 2016
Publisher: IEEE
Abstract: Sequence 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.
Description: 
URI: http://localhost/handle/Hannan/156339
http://localhost/handle/Hannan/629367
ISSN: 1041-4347
volume: 28
issue: 5
Appears in Collections:2016

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