SD
S. Dieck
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Ensemble Techniques for PDFA Learning
Diversity-Driven Ensemble Learning with the Alergia Algorithm
Probabilistic deterministic Finite Automata (PDFA) learning is a machine learning method used for tasks requiring human understandability and more formal validation. In recent years we saw numerous applications of ensemble techniques with other machine learning models such as decision trees. Following the success of these attempts, in this paper, we aim to integrate ensemble methods into Alergia, which is a famous algorithm in the PDFA learning realm. We present a randomized variation of the Alergia algorithm and show how to build an ensemble out of it. Such an ensemble can visibly outperform a single Alergia model, which is documented by a series of experiments. Next, we present a custom distance metric measuring dissimilarity between a pair of Alergia models. We show how it can be used to build an Inter-Model Variety score quantifying the overall diversity of a group of models. Lastly, we analyze several methods that strive to select a well-performing diverse ensemble out of a big population of generated models.
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Probabilistic deterministic Finite Automata (PDFA) learning is a machine learning method used for tasks requiring human understandability and more formal validation. In recent years we saw numerous applications of ensemble techniques with other machine learning models such as decision trees. Following the success of these attempts, in this paper, we aim to integrate ensemble methods into Alergia, which is a famous algorithm in the PDFA learning realm. We present a randomized variation of the Alergia algorithm and show how to build an ensemble out of it. Such an ensemble can visibly outperform a single Alergia model, which is documented by a series of experiments. Next, we present a custom distance metric measuring dissimilarity between a pair of Alergia models. We show how it can be used to build an Inter-Model Variety score quantifying the overall diversity of a group of models. Lastly, we analyze several methods that strive to select a well-performing diverse ensemble out of a big population of generated models.
Ensemble Techniques for DFA Learning
DFA Ensembles without Suitability Metrics
Deterministic Finite Automata (DFAs) are interpretable classification models, typically learned through merging states of a large tree-like automaton, an Augmented Prefix Tree Acceptor (APTA), according to heuristic suitability metrics. This paper introduces an ensembling approach for DFAs that does not depend on such heuristics. Starting from the APTA, we construct diverse automata by applying randomized sequences of state merges, while avoiding repetition of merges whenever possible. We also propose a novel graph-connectivity-based metric for inter-model variety. Experimental results on the STAMINA competition datasets yield improved predictive performance compared to models learned using state-of-the-art heuristics on sparse datasets, as well as a tight connection between inter-model variety and performance.
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Deterministic Finite Automata (DFAs) are interpretable classification models, typically learned through merging states of a large tree-like automaton, an Augmented Prefix Tree Acceptor (APTA), according to heuristic suitability metrics. This paper introduces an ensembling approach for DFAs that does not depend on such heuristics. Starting from the APTA, we construct diverse automata by applying randomized sequences of state merges, while avoiding repetition of merges whenever possible. We also propose a novel graph-connectivity-based metric for inter-model variety. Experimental results on the STAMINA competition datasets yield improved predictive performance compared to models learned using state-of-the-art heuristics on sparse datasets, as well as a tight connection between inter-model variety and performance.