On the Learning with Augmented Class via Forests
This addresses a domain-specific challenge in machine learning for handling unseen classes in classification tasks, representing an incremental advance.
The paper tackles the problem of learning with augmented classes in decision forests, where testing data includes classes not seen during training, by introducing an augmented Gini impurity criterion and developing LACForest and deep neural forest approaches, achieving improved performance in experiments.
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.