RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines
Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background,...
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| Published in | Computer graphics forum Vol. 41; no. 6; pp. 302 - 315 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.09.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 1467-8659 |
| DOI | 10.1111/cgf.14452 |
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| Abstract | Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decision‐making process. RfX allows to interactively analyse the properties of a forest and to explore and compare multiple trees in a RF. Thus, its users can identify relationships within a RF's feature subspace and detect hidden patterns in the model's underlying data. We contribute a design study in collaboration with an automotive company. A formative evaluation of RFX was carried out with two domain experts and a summative evaluation in the form of a field study with five domain experts. In this context, new hidden patterns such as increased eccentricities in an engine's rotor by observing secondary excitations of its bearings were detected using analyses made with RfX. Rules derived from analyses with the system led to a change in the company's testing procedures for electrical engines, which resulted in 80% reduced testing time for over 30% of all components.
Random Forests (RFs) are a machine learning (ML) technique widely used across industries. |
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| AbstractList | Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decision‐making process. RfX allows to interactively analyse the properties of a forest and to explore and compare multiple trees in a RF. Thus, its users can identify relationships within a RF's feature subspace and detect hidden patterns in the model's underlying data. We contribute a design study in collaboration with an automotive company. A formative evaluation of RFX was carried out with two domain experts and a summative evaluation in the form of a field study with five domain experts. In this context, new hidden patterns such as increased eccentricities in an engine's rotor by observing secondary excitations of its bearings were detected using analyses made with RfX. Rules derived from analyses with the system led to a change in the company's testing procedures for electrical engines, which resulted in 80% reduced testing time for over 30% of all components. Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decision‐making process. RfX allows to interactively analyse the properties of a forest and to explore and compare multiple trees in a RF. Thus, its users can identify relationships within a RF's feature subspace and detect hidden patterns in the model's underlying data. We contribute a design study in collaboration with an automotive company. A formative evaluation of RFX was carried out with two domain experts and a summative evaluation in the form of a field study with five domain experts. In this context, new hidden patterns such as increased eccentricities in an engine's rotor by observing secondary excitations of its bearings were detected using analyses made with RfX. Rules derived from analyses with the system led to a change in the company's testing procedures for electrical engines, which resulted in 80% reduced testing time for over 30% of all components. Random Forests (RFs) are a machine learning (ML) technique widely used across industries. |
| Author | Münch, M. Jäckle, D. Bonart, J. Sedlmair, M. Schreck, T. Eirich, J. |
| Author_xml | – sequence: 1 givenname: J. surname: Eirich fullname: Eirich, J. email: joscha.eirich@uni-bamberg.de organization: BMW Group – sequence: 2 givenname: M. surname: Münch fullname: Münch, M. organization: Universtiy of Ulm – sequence: 3 givenname: D. surname: Jäckle fullname: Jäckle, D. organization: BMW Group – sequence: 4 givenname: M. surname: Sedlmair fullname: Sedlmair, M. organization: University of Stuttgart – sequence: 5 givenname: J. surname: Bonart fullname: Bonart, J. organization: Fraunhofer IWU – sequence: 6 givenname: T. surname: Schreck fullname: Schreck, T. organization: Graz University of Technology |
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| SubjectTerms | Automobile industry Automotive engines Data analysis Decision analysis Decision trees Domains human–computer interfaces interaction Machine learning Mathematical analysis Subject specialists Test procedures Testing time visual analytics visualization |
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| Title | RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines |
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