Filter based Taxonomy Modification for Improving Hierarchical
  Classification
  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.
