Wu, Feihong (2005) Learning Hierarchical Classifiers with Class Taxonomies. Technical Report, Computer Science Department, Iowa State University.
As more and more data with class taxonomies emerge in diverse
fields, such as pattern recognition, text classification and gene
function prediction, we need to extend traditional machine
learning methods to solve classification problem in such data
sets, which presents more challenges over common pattern
classification problems. In this paper, we define structured label
classification problem and investigate two learning approaches
that can learn classifier in such data sets. We also develop
distance metrics with label mapping strategy to evaluate the
results. We present experimental results that demonstrate the
promise of the proposed approaches.
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