Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data

We consider the problem of learning to classify partially specified instances i.e., instances that are described in terms of attribute values at different levels of precision, using user-supplied attribute value taxonomies (AVT). We formalize the problem of learning from AVT and data and present an AVT-guided decision tree learning algorithm (AVT-DTL) to learn classification rules at multiple levels of abstraction. The proposed approach generalizes existing techniques for dealing with missing values to handle instances with partially missing values. We present experimental results that demonstrate that AVT-DTL is able to effectively learn robust high accuracy classifiers from partially specified examples. Our experiments also demonstrate that the use of AVT-DTL outperforms standard decision tree algorithm (C4.5 and its variants) when applied to data with missing attribute values; and produces substantially more compact decision trees than those obtained by standard approach.