Behavioral Constraint Template-Based Sequence Classification

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 10535; pp. 20 - 36
• Main Authors: De Smedt, Johannes, Deeva, Galina, De Weerdt, Jochen
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Constraint-based mining; Sequence classification; Sequence mining
• ISBN: 9783319712451; 3319712454
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-71246-8_2

In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification. Code related to this chapter is available at: http://feb.kuleuven.be/public/u0092789/.