Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement
Figures supplement
Figure 1. Calculation of the sum of the distances between a minority sample and its k-nearest minority neighbors (k=3)
Figure 2. Demonstration of the loneliness function
Figure 3. Finding local variation
Figure 4. Example of synthetic sample generation on a hypothetical 3-dimensional dataset
Figure 5. (a) Large 
leads
to most synthetic points generated around loneliest minority points
Figure 5. (b) 
leads
to synthetic points equally distributed for all existing minority points
Figure 5. (c) Larger k results in larger spread for synthetic points around their respective originating minority points
Figure 5. (d) Changing k
Figure 5. (e) Changing Lambda
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Figure 6. (a) Original data (left) with prediction map (right) |
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Figure 6. (b) Overlapped data |
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Figure 6. (c) Overlapped data oversampled with SMOTE |
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Figure 6. (d) Overlapped data oversampled with MWMOTE |
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Figure 6. (e) Overlapped data oversampled with ADASYN |
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Figure 6. (f) Overlapped data oversampled with BSMOTE |
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Figure 6. (g) Overlapped data oversampled with PDFOS |
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Figure 6. (h) Overlapped data oversampled with RWOS |
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Figure 6. (i) Overlapped data oversampled with SANSA |
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Figure 7. (a) Original data (left) with prediction map (right) |
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Figure 7. (b) Sparse data |
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Figure 7. (c) Sparse data oversampled with SMOTE |
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Figure 7. (d) Sparse data oversampled with MWMOTE |
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Figure 7. (e) Sparse data oversampled with ADASYN |
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Figure 7. (f) Sparse data oversampled with BSMOTE |
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Figure 7. (g) Sparse data oversampled with PDFOS |
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Figure 7. (h) Sparse data oversampled with RWOS |
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Figure 7. (i) Sparse data oversampled with SANSA |
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Figure 8. Imbalanced Learning Framework using SANSA