HierLPR: Decision making in hierarchical multi-label classification with local precision rates
This addresses hierarchical classification problems, such as disease diagnosis, but is incremental as it builds on existing ranking methods with a new metric.
The authors tackled hierarchical multi-label classification by proposing HierLPR, a ranking algorithm that optimizes a new eAUC metric, showing favorable results in early precision-recall on synthetic and disease diagnosis datasets.
In this article we propose a novel ranking algorithm, referred to as HierLPR, for the multi-label classification problem when the candidate labels follow a known hierarchical structure. HierLPR is motivated by a new metric called eAUC that we design to assess the ranking of classification decisions. This metric, associated with the hit curve and local precision rate, emphasizes the accuracy of the first calls. We show that HierLPR optimizes eAUC under the tree constraint and some light assumptions on the dependency between the nodes in the hierarchy. We also provide a strategy to make calls for each node based on the ordering produced by HierLPR, with the intent of controlling FDR or maximizing F-score. The performance of our proposed methods is demonstrated on synthetic datasets as well as a real example of disease diagnosis using NCBI GEO datasets. In these cases, HierLPR shows a favorable result over competing methods in the early part of the precision-recall curve.