Publication
Title
Itemset based sequence classification
Author
Abstract
Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting itemsets found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of an itemset in a given class of sequences by combining the cohesion and the support of the itemset. We use the discovered itemsets to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on the CBA (Classification based on associations) method, but we use a new ranking strategy for the generated rules, achieving better results. The second classifier ranks the rules by first measuring their value specific to the new data object. Experimental results show that our classifiers outperform existing comparable classifiers in terms of accuracy and stability, while maintaining a computational advantage over sequential pattern based classification.
Language
English
Source (journal)
Lecture notes in computer science
Publication
2013
Volume/pages
8188(2013), p. 353-368
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 12.04.2016
Last edited 13.04.2016
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