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Architectural Planning and Design

Style classification and prediction of residential buildings based on machine learning

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Pages 714-730 | Received 16 Jan 2020, Accepted 29 May 2020, Published online: 15 Jul 2020

Figures & data

Figure 1. Co-keywords network analysis concerning architectural styles

Figure 1. Co-keywords network analysis concerning architectural styles

Figure 2. Research framework

Figure 2. Research framework

Table 1. Examples of questions and answers in structured interview

Table 2. Preliminary morphological elements of residential building style

Table 3. The site economic factors affecting style positioning

Table 4. Assignment method of key style morphological elements

Figure 3. RBF-NN model Structure

Figure 3. RBF-NN model Structure

Figure 4. Importance scores of morphological elements

Figure 4. Importance scores of morphological elements

Figure 5. Importance scores of site economics factors

Figure 5. Importance scores of site economics factors

Table 5. Automatic clustering process

Figure 6. Clustering importance ranking of variables

Figure 6. Clustering importance ranking of variables

Table 6. Quantitative distribution of variables in eight styles

Figure 7. Two-dimension corresponding analysis

Figure 7. Two-dimension corresponding analysis

Table 7. The feature sample of eight style categories

Table 8. Coefficients of Fisher’s linear discriminant functions

Table 9. Comparison of popular styles

Table 10. Correlation matrix of economic factors and morphological elements

Figure 8. RBF-NN prediction accuracy

Figure 8. RBF-NN prediction accuracy