Filter based Taxonomy Modification for Improving Hierarchical Classification
This work addresses the issue of suboptimal hierarchical structures in classification tasks, particularly benefiting domains with large-scale or imbalanced data, though it is incremental as it builds on existing rewiring methods.
The paper tackles the problem of inconsistent expert-defined taxonomies in hierarchical classification by proposing a scalable data-driven filter-based rewiring approach to modify hierarchies, resulting in improved classification performance across various datasets and for classes with few training samples.
Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down HC with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art HC approaches.