Applying Backtracking in Hierarchical Classification to Recover from Error Propagation

Abstract

Hierarchical classification has been previously demonstrated to yield more ac- curate results in comparison to flat classifiers; however, the tree-like structure intro- duces the problem of error propagation. Error propagation happens when a parent classifier within the hierarchy misclassifies an object. In traditional hierarchical structures, the system cannot recover from such misclassifications. In this paper, we propose a method of backtracking that allows the system to recover from such errors. For each non-root classifier in the structure, we add a new label option, "incorrect”. In the case that an object is classified as "incorrect” we perform back- tracking up the hierarchy and reclassify the object to the next best class from that previous level of the hierarchy. Through experimentation we have found that this method can help the system recover from individual instances of error propagation; however, more work is necessary to find an appropriate weight to give the "incorrect” class that doesn’t cause other errors, decreasing the over-all accuracy.