Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts
During the design stage, quick and accurate predictions are required for effective design decisions. Model developers prefer simple interpretable models for high computation speed. Given that deep learning (DL) has high computational speed and accuracy, it will be beneficial if these models are expl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2020-01-01
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| Series: | Design Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2053470120000220/type/journal_article |
| _version_ | 1811156470494920704 |
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| author | Sundaravelpandian Singaravel Johan Suykens Hans Janssen Philipp Geyer |
| author_facet | Sundaravelpandian Singaravel Johan Suykens Hans Janssen Philipp Geyer |
| author_sort | Sundaravelpandian Singaravel |
| collection | DOAJ |
| description | During the design stage, quick and accurate predictions are required for effective design decisions. Model developers prefer simple interpretable models for high computation speed. Given that deep learning (DL) has high computational speed and accuracy, it will be beneficial if these models are explainable. Furthermore, current DL development tools simplify the model development process. The article proposes a method to make the learning of the DL model explainable to enable non–machine learning (ML) experts to infer on model generalization and reusability. The proposed method utilizes dimensionality reduction (t-Distribution Stochastic Neighbour Embedding) and mutual information (MI). Results indicate that the convolutional layers capture design-related interpretations, and the fully connected layer captures performance-related interpretations. Furthermore, the global geometric structure within a model that generalized well and poorly is similar. The key difference indicating poor generalization is smoothness in the low-dimensional embedding. MI enables quantifying the reason for good and poor generalization. Such interpretation adds more information on model behaviour to a non-ML expert. |
| first_indexed | 2024-04-10T04:51:05Z |
| format | Article |
| id | doaj.art-fddccc4bd41144ff971b7ef1f009b20a |
| institution | Directory Open Access Journal |
| issn | 2053-4701 |
| language | English |
| last_indexed | 2024-04-10T04:51:05Z |
| publishDate | 2020-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Design Science |
| spelling | doaj.art-fddccc4bd41144ff971b7ef1f009b20a2023-03-09T12:32:01ZengCambridge University PressDesign Science2053-47012020-01-01610.1017/dsj.2020.22Explainable deep convolutional learning for intuitive model development by non–machine learning domain expertsSundaravelpandian Singaravel0https://orcid.org/0000-0002-4645-3983Johan Suykens1Hans Janssen2Philipp Geyer3Architectural Engineering Division, KU Leuven, Leuven, BelgiumESAT-STADIUS, KU Leuven, Leuven, BelgiumDepartment of Civil Engineering, Building Physics Section, KU Leuven, Leuven, BelgiumArchitectural Engineering Division, KU Leuven, Leuven, BelgiumDuring the design stage, quick and accurate predictions are required for effective design decisions. Model developers prefer simple interpretable models for high computation speed. Given that deep learning (DL) has high computational speed and accuracy, it will be beneficial if these models are explainable. Furthermore, current DL development tools simplify the model development process. The article proposes a method to make the learning of the DL model explainable to enable non–machine learning (ML) experts to infer on model generalization and reusability. The proposed method utilizes dimensionality reduction (t-Distribution Stochastic Neighbour Embedding) and mutual information (MI). Results indicate that the convolutional layers capture design-related interpretations, and the fully connected layer captures performance-related interpretations. Furthermore, the global geometric structure within a model that generalized well and poorly is similar. The key difference indicating poor generalization is smoothness in the low-dimensional embedding. MI enables quantifying the reason for good and poor generalization. Such interpretation adds more information on model behaviour to a non-ML expert.https://www.cambridge.org/core/product/identifier/S2053470120000220/type/journal_articlemodel explorationdesign space representationreasoning |
| spellingShingle | Sundaravelpandian Singaravel Johan Suykens Hans Janssen Philipp Geyer Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts Design Science model exploration design space representation reasoning |
| title | Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts |
| title_full | Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts |
| title_fullStr | Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts |
| title_full_unstemmed | Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts |
| title_short | Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts |
| title_sort | explainable deep convolutional learning for intuitive model development by non machine learning domain experts |
| topic | model exploration design space representation reasoning |
| url | https://www.cambridge.org/core/product/identifier/S2053470120000220/type/journal_article |
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