  Large-scale classification of data where classes are structurally organized
in a hierarchy is an important area of research. Top-down approaches that
exploit the hierarchy during the learning and prediction phase are efficient
for large scale hierarchical classification. However, accuracy of top-down
approaches is poor due to error propagation i.e., prediction errors made at
higher levels in the hierarchy cannot be corrected at lower levels. One of the
main reason behind errors at the higher levels is the presence of inconsistent
nodes that are introduced due to the arbitrary process of creating these
hierarchies by domain experts. In this paper, we propose two different
data-driven approaches (local and global) for hierarchical structure
modification that identifies and flattens inconsistent nodes present within the
hierarchy. Our extensive empirical evaluation of the proposed approaches on
several image and text datasets with varying distribution of features, classes
and training instances per class shows improved classification performance over
competing hierarchical modification approaches. Specifically, we see an
improvement upto 7% in Macro-F1 score with our approach over best TD baseline.
SOURCE CODE: http://www.cs.gmu.edu/~mlbio/InconsistentNodeFlattening
