An Experimental Evaluation of the Generalizing Capabilities of Process Discovery Techniques and Black-Box Sequence Models An Experimental Evaluation of the Generalizing Capabilities of Process Discovery Techniques and Black-Box Sequence Models
Paper summary # Contributions The contribution of this paper is three-fold: 1. We present a method to use *process models* as interpretable sequence models that have a stronger notion of interpretability than what is generally used in the machine learning field (see Section *process models* below), 2. We show that this approach enables the comparison of traditional sequence models (RNNs, LSTMs, Markov Models) with techniques from the research field of *automated process discovery*, 3. We show on a collection of three real-life datasets that a better fit of sequence data can be obtained with LSTMs than with techniques from the *automated process discovery* field # Process Models Process models are visually interpretable models that model sequence data in such a way that the generated model is represented in a notation that has *formal semantics*, i.e., it is well-defined which sequences are and which aren't allowed by the model. Below you see an example of a Petri net (a type of model with formal semantics) which allows for the sequences <A,B,C>, <A,C,B>, <D,B,C>, and <D,C,B>. https://i.imgur.com/SbVYMvX.png For an overview of automated process discovery algorithms to mine a process model from sequnce data, we refer to [this recent survey and benchmark paper](https://ieeexplore.ieee.org/abstract/document/8368306/).
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An Experimental Evaluation of the Generalizing Capabilities of Process Discovery Techniques and Black-Box Sequence Models
Niek Tax and Sebastiaan J. van Zelst and Irene Teinemaa
Lecture Notes in Business Information Processing - 2018 via Local CrossRef
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