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+
+@inproceedings{bombara_decision_2016,
+	address = {New York, NY, USA},
+	series = {{HSCC} '16},
+	title = {A {Decision} {Tree} {Approach} to {Data} {Classification} {Using} {Signal} {Temporal} {Logic}},
+	isbn = {978-1-4503-3955-1},
+	url = {http://doi.acm.org/10.1145/2883817.2883843},
+	doi = {10.1145/2883817.2883843},
+	abstract = {This paper introduces a framework for inference of timed temporal logic properties from data. The dataset is given as a finite set of pairs of finite-time system traces and labels, where the labels indicate whether the traces exhibit some desired behavior (e.g., a ship traveling along a safe route). We propose a decision-tree based approach for learning signal temporal logic classifiers. The method produces binary decision trees that represent the inferred formulae. Each node of the tree contains a test associated with the satisfaction of a simple formula, optimally tuned from a predefined finite set of primitives. Optimality is assessed using heuristic impurity measures, which capture how well the current primitive splits the data with respect to the traces' labels. We propose extensions of the usual impurity measures from machine learning literature to handle classification of system traces by leveraging upon the robustness degree concept. The proposed incremental construction procedure greatly improves the execution time and the accuracy compared to existing algorithms. We present two case studies that illustrate the usefulness and the computational advantages of the algorithms. The first is an anomaly detection problem in a maritime environment. The second is a fault detection problem in an automotive powertrain system.},
+	booktitle = {Proceedings of the 19th {International} {Conference} on {Hybrid} {Systems}: {Computation} and {Control}},
+	publisher = {ACM},
+	author = {Bombara, Giuseppe and Vasile, Cristian-Ioan and Penedo, Francisco and Yasuoka, Hirotoshi and Belta, Calin},
+	year = {2016},
+	keywords = {femformal, signal temporal logic, anomaly detection, decision trees, impurity measure, logic inference, machine learning, supervised learning},
+	pages = {1--10},
+	file = {ACM Full Text PDF:/home/fran/.zotero/zotero/th89c3ji.default/zotero/storage/MIBCBJM6/Bombara et al. - 2016 - A Decision Tree Approach to Data Classification Us.pdf:application/pdf}
+}
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+---
+title: "A Decision Tree Approach to Data Classification Using Signal Temporal Logic"
+
+# Authors
+# If you created a profile for a user (e.g. the default `admin` user), write the username (folder name) here 
+# and it will be replaced with their full name and linked to their profile.
+authors:
+- Giuseppe Bombara
+- Cristian-Ioan Vasile
+- admin
+- Hirotoshi Yasuoka
+- Calin Belta
+
+# Author notes (optional)
+# author_notes:
+# - "Equal contribution"
+# - "Equal contribution"
+
+date: "2016-04-01"
+doi: "https://doi.org/10.1145/2883817.2883843"
+
+# Schedule page publish date (NOT publication's date).
+# publishDate: "2017-01-01T00:00:00Z"
+
+# Publication type.
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+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ["1"]
+
+# Publication name and optional abbreviated publication name.
+publication: In Proceedings of the 19th International Conference on Hybrid Systems, Computation and Control 2016
+publication_short: In HSCC 2016
+
+abstract: This paper introduces a framework for inference of timed temporal logic properties from data. The dataset is given as a finite set of pairs of finite-time system traces and labels, where the labels indicate whether the traces exhibit some desired behavior (e.g., a ship traveling along a safe route). We propose a decision-tree based approach for learning signal temporal logic classifiers. The method produces binary decision trees that represent the inferred formulae. Each node of the tree contains a test associated with the satisfaction of a simple formula, optimally tuned from a predefined finite set of primitives. Optimality is assessed using heuristic impurity measures, which capture how well the current primitive splits the data with respect to the traces' labels. We propose extensions of the usual impurity measures from machine learning literature to handle classification of system traces by leveraging upon the robustness degree concept. The proposed incremental construction procedure greatly improves the execution time and the accuracy compared to existing algorithms. We present two case studies that illustrate the usefulness and the computational advantages of the algorithms. The first is an anomaly detection problem in a maritime environment. The second is a fault detection problem in an automotive powertrain system.
+
+# Summary. An optional shortened abstract.
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+
+tags: []
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