Bottom-up Hierarchical Classification Using Confusion-based Logit Compression
This addresses the challenge of reliable posterior estimation in hierarchical classification for domains with scarce validation examples, though it is incremental.
The paper tackles the problem of estimating label posteriors in hierarchical classification with limited validation data by proposing a logit compression method based on generalized logits and label confusions, achieving strong performance across various validation set sizes.
In this work, we propose a method to efficiently compute label posteriors of a base flat classifier in the presence of few validation examples within a bottom-up hierarchical inference framework. A stand-alone validation set (not used to train the base classifier) is preferred for posterior estimation to avoid overfitting the base classifier, however a small validation set limits the number of features one can effectively use. We propose a simple, yet robust, logit vector compression approach based on generalized logits and label confusions for the task of label posterior estimation within the context of hierarchical classification. Extensive comparative experiments with other compression techniques are provided across multiple sized validation sets, and a comparison with related hierarchical classification approaches is also conducted. The proposed approach mitigates the problem of not having enough validation examples for reliable posterior estimation while maintaining strong hierarchical classification performance.