Kavli Affiliate: Wei Gao
| First 5 Authors: Fan Xu, Wuyang Chen, Wei Gao, ,
| Summary:
Decision trees and forests have achieved successes in various real
applications, most working with all testing classes known in training data. In
this work, we focus on learning with augmented class via forests, where an
augmented class may appear in testing data yet not in training data. We
incorporate information of augmented class into trees’ splitting, i.e., a new
splitting criterion, called augmented Gini impurity, is introduced to exploit
some unlabeled data from testing distribution. We then develop the approach
named Learning with Augmented Class via Forests (LACForest), which constructs
shallow forests based on the augmented Gini impurity and then splits forests
with pseudo-labeled augmented instances for better performance. We also develop
deep neural forests with a novel optimization objective based on our augmented
Gini impurity, so as to utilize the representation power of neural networks for
forests. Theoretically, we present the convergence analysis for augmented Gini
impurity, and finally conduct experiments to verify the effectiveness of our
approaches. The code is available at https://github.com/nju-xuf/LACForest/.
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