Title: Fast Meta-Learning for Adaptive Hierarchical Classifier Design,
Abstract: We propose a new splitting criterion for a meta-learning approach to
multiclass classifier design that adaptively merges the classes into a
tree-structured hierarchy of increasingly difficult binary classification
problems. The classification tree is constructed from empirical estimates of
the Henze-Penrose bounds on the pairwise Bayes misclassification rates that
rank the binary subproblems in terms of difficulty of classification. The
proposed empirical estimates of the Bayes error rate are computed from the
minimal spanning tree (MST) of the samples from each pair of classes. Moreover,
a meta-learning technique is presented for quantifying the one-vs-rest Bayes
error rate for each individual class from a single MST on the entire dataset.
Extensive simulations on benchmark datasets show that the proposed hierarchical
method can often be learned much faster than competing methods, while achieving
competitive accuracy.