Kavli Affiliate: Wei Gao
| First 5 Authors: Fan Xu, Fan Xu, , ,
| 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, that is,
augmented Gini impurity, a new splitting criterion is introduced to exploit
some unlabeled data from testing distribution. We then develop the Learning
with Augmented Class via Forests (short for LACForest) approach, which
constructs shallow forests according to the augmented Gini impurity and then
splits forests with pseudo-labeled augmented instances for better performance.
We also develop deep neural forests via an optimization objective based on our
augmented Gini impurity, which essentially utilizes the representation power of
neural networks for forests. Theoretically, we present the convergence analysis
for our augmented Gini impurity, and we finally conduct experiments to evaluate
our approaches. The code is available at https://github.com/nju-xuf/LACForest.
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