###Optimally combining classifiers for semi-supervised learning
We propose a new semi-supervised method combing Xgboost and transductive support vector machine.
Experiments on 14 UCI data
- Python 3.6+
- pandas
- matplotlib
- numpy
- Pycharm
Obtain the visualization of the real data by T-SNE
python DataDistribution.py
Compare the diversity of Xgboost, TSVM, DecisionTree
python compare_diversity.py
Run the proposed method for 14 real data
python new_algorithm_test
Data | cjs | hill | segment | wdbc | steel | analcat | synthetic |
---|---|---|---|---|---|---|---|
Accuracy | 0.98 | 0.499 | 0.925 | 0.954 | 0.649 | 0.993 | 0.92 |
Data | vehicle | german | gina | madelon | texture | gas-grift | dna |
---|---|---|---|---|---|---|---|
Accuracy | 0.625 | 0.716 | 0.857 | 0.543 | 0.953 | 0.965 | 0.911 |