Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Sequence Classification Model Based on Pattern Coverage Rate
verfasst von : I-Hui Li, Jyun-Yao Huang, I-En Liao, Jin-Han Lin
Erschienen in: Grid and Pervasive Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The technique of classification can sort data into various categories for data mining studies. The demand for sequence data classification has increased with the development of information technology. Several applications involve decision prediction based on sequence data, but the traditional classification methods are unsuitable for sequence data. Thus, this paper proposes a Pattern Coverage Rate-based Sequence Classification Model (PCRSCM) to integrate sequential pattern mining and classification techniques. PCRSCM mines sequential patterns to find characteristics of each class, and then calculates pattern coverage rates and class scores to predict the class of a sequence. The experimental results show that PCRSCM exhibits excellent prediction performance on synthetic and real sequence data.