Behavioral Constraint Template-Based Sequence Classification
Abstract
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/ .
Cite
Text
De Smedt et al. "Behavioral Constraint Template-Based Sequence Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_2Markdown
[De Smedt et al. "Behavioral Constraint Template-Based Sequence Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/smedt2017ecmlpkdd-behavioral/) doi:10.1007/978-3-319-71246-8_2BibTeX
@inproceedings{smedt2017ecmlpkdd-behavioral,
title = {{Behavioral Constraint Template-Based Sequence Classification}},
author = {De Smedt, Johannes and Deeva, Galina and De Weerdt, Jochen},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {20-36},
doi = {10.1007/978-3-319-71246-8_2},
url = {https://mlanthology.org/ecmlpkdd/2017/smedt2017ecmlpkdd-behavioral/}
}