Mining Behavioral Sequence Constraints for Classification

Johannes De Smedt

doi:10.1109/TKDE.2019.2897311

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Mining Behavioral Sequence Constraints for Classification

2020-06-01

Johannes De Smedt *

Galina Deeva

Jochen De Weerdt

DOI：10.1109/TKDE.2019.2897311

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Abstract

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Sequence classification deals with the task of finding discriminative and concise sequential patterns. To this purpose, many techniques have been proposed, which mainly resort to the use of partial orders to capture the underlying sequences in a database according to the labels. Partial orders, however, pose many limitations, especially on expressiveness, i.e., the aptitude towards capturing certain behavior, and on conciseness, i.e., doing so in a compact and informative way. These limitations can be addressed by using a better representation. In this paper, we present the interesting Behavioral Constraint Miner (iBCM), a sequence classification technique that discovers patterns using behavioral constraint templates. The templates comprise a variety of constraints and can express patterns ranging from simple occurrence, to looping and position-based behavior over a sequence. Furthermore, iBCM also captures negative constraints, i.e., absence of particular behavior. The constraints can be discovered by using simple string operations in an efficient way. Finally, deriving the constraints with a window-based approach allows to pinpoint where the constraints hold in a string, and to detect whether patterns are subject to concept drift. Through empirical evaluation, it is shown that iBCM is better capable of classifying sequences more accurately and concisely in a scalable manner.

Keywords:

Databases

Data mining

Feature extraction

Distance measurement

Knowledge based systems

Task analysis

Sequence classification

sequential pattern mining

behavioral constraint templates

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