Abstract
In this paper, we study the problem of classification of sequences of temporal intervals. Our main contribution is a novel framework, which we call SMILE, for extracting relevant features from interval sequences to construct classifiers.SMILE introduces the notion of utilizing random temporal abstraction features, we define as e-lets, as a means to capture information pertaining to class-discriminatory events which occur across the span of complete interval sequences. Our empirical evaluation is applied to a wide array of benchmark data sets and fourteen novel datasets for adverse drug event detection. We demonstrate how the introduction of simple sequential features, followed by progressively more complex features each improve classification performance. Importantly, this investigation demonstrates that SMILE significantly improves AUC performance over the current state-of-the-art. The investigation also reveals that the selection of underlying classification algorithm is important to achieve superior predictive performance, and how the number of features influences the performance of our framework.
Highlights
Sequences of temporal intervals are defined as ordered sets of events occurring over time, with each event having a time duration, which may co-occur with other events
The key novelty of SMILE is that it reduces the complexity of event-interval sequences by performing four levels of temporal abstraction, starting from simple, global abstraction features and gradually moving to more complex local class-predictive temporal features. These features take into consideration both the temporal relation types between the event-intervals, as well as their time duration. – We introduce and define a new concept for temporal interval sequence classification, which we refer to as e-lets
The remainder of this paper is organized as follows: in Sect. 2 we present the related work in the area of temporal interval sequence classification, while in Sect. 3 we formalize the classification problem studied in this paper along with the required technical background and definitions
Summary
Sequences of temporal intervals are defined as ordered sets of events occurring over time, with each event having a time duration, which may co-occur with other events. Several temporal relations are possible between pairs of events, such as one event overlapping another event or two events starting concurrently with one ending before the other. Such sequences, known as e-sequences, can be found in a variety of application domains, including sign language transcription (Papapetrou et al 2009), human activity recognition and monitoring (Uddin and Uddiny 2015), music classification (Pachet et al 1996), and predicting clinical outcomes from medical records (Kosara and Miksch 2001; Moskovitch and Shahar 2015a). The patient developed ventricular tachycardia (last event) which is an ADR in relation to procainamide
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