Post-Operative Patient Data Set
The classification task of this database is to determine where patients in a postoperative recovery area should be sent to next. Because hypothermia is a significant concern after surgery (Woolery, L. et. al. 1991), the attributes correspond roughly to body temperature measurements.
Results: -- LERS (LEM2): 48% accuracy
| - | - |
|---|---|
| Data Set Characteristics | Multivariate |
| Attribute Characteristics | Categorical, Integer |
| Number of Attributes | 8 |
| Number of Instances | 90 |
| Associated Tasks | Classification |
| Missing Values? | Yes |
- Creators:
Sharon Summers, School of Nursing, University of Kansas Medical Center, Kansas City, KS 66160 Linda Woolery, School of Nursing, University of Missouri, Columbia, MO 65211
- Donor:
Jerzy W. Grzymala-Busse (jerzy '@' cs.ukans.edu) (913)864-4488
Paper - Rule extraction from linear support vector machines
Measure the accuracy of the test subset (30% of instances)
| Model | Kernel | Decision Function | Accuracy | Remark |
|---|---|---|---|---|
| SVM Scikit Learn | RBF(default) | OVO & OVR | 0.7407 | use OVO and OVR are the same |
Using simplified binary dataset (label I -> S)
| Model | Kernel | Accuracy | Remark |
|---|---|---|---|
| SVM From Scratch (using cvxopt) | Linear & RBF & Polynomial | 0.7407 | use Linear RBF and Polynomial kernel are the same |