Fast Meta-Learning for Adaptive Hierarchical Classifier Design
This work addresses the problem of efficient multiclass classification for machine learning practitioners, offering a faster learning approach with incremental improvements in speed.
The paper tackles the problem of designing multiclass classifiers by adaptively merging classes into a tree-structured hierarchy of binary classification problems, using empirical estimates of Bayes misclassification rates from minimal spanning trees. The result shows that the proposed hierarchical method can be learned much faster than competing methods while achieving competitive accuracy on benchmark datasets.
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.