$$AI^{2}$$: the next leap toward native language-based and explainable machine learning framework

Dessureault, Jean-Sébastien; Massicotte, Daniel

doi:10.1007/s10515-023-00399-5

Autom Softw Eng

2023

DOI: 10.1007/s10515-023-00399-5

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$$AI^{2}$$: the next leap toward native language-based and explainable machine learning framework

Jean-Sébastien Dessureault,

Daniel Massicotte

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2024

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Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs

Dessureault,

Clément,

et al. 2024

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The field of interior home design has witnessed a growing utilization of machine learning. However, the subjective nature of aesthetics poses a significant challenge due to its variability among individuals and cultures. This paper proposes an applied machine learning method to enhance manufactured custom doors in a proper and aesthetic home design environment. Since there are millions of possible custom door models based on door types, wood species, dyeing, paint, and glass types, it is impossible to foresee a home design model fitting every custom door. To generate the classification data, a home design expert has to label thousands of door/home design combinations with the different colors and shades utilized in home designs. These data train a random forest classifier in a supervised learning context. The classifier predicts a home design according to a particular custom door. This method is applied in the following context: A web page displays a choice of doors to a customer. The customer selects the desired door properties, which are sent to a server that returns an aesthetic home design model for this door. This door configuration generates a series of images through the Unity 3D engine module, which are returned to the web client. The customer finally visualizes their door in an aesthetic home design context. The results show the random forest classifier’s good performance, with an accuracy level of 86.8%, in predicting suitable home design, marking the way for future developments requiring subjective evaluations. The results are also explained using a feature importance graphic, a decision tree, a confusion matrix, and text.

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Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs

Dessureault,

Clément,

et al. 2024

Information

Self Cite

View full text Add to dashboard Cite

show abstract

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$$AI^{2}$$: the next leap toward native language-based and explainable machine learning framework

Cited by 1 publication

References 32 publications

Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs

Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs

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