The proliferation of topic hierarchies for text documents has resulted in a
need for tools that automatically classify new documents within such
hierarchies.  Existing classification schemes which ignore the
hierarchical structure and treat the topics as separate classes are
often inadequate in text classification where the there is a large number of
classes and a huge number of relevant features needed to distinguish
between them.  We propose an approach that utilizes the hierarchical
topic structure to decompose the classification task into a set of
simpler problems, one at each node in the classification tree.  As we
show, each of these smaller problems can be solved accurately by
focusing only on a very small set of features, those relevant to the
task at hand.  This set of relevant features varies widely throughout
the hierarchy, so that, while the overall relevant feature set may be
large, each classifier only examines a small subset.  The use of
reduced feature sets allows us to utilize more complex (probabilistic)
models, without encountering many of the standard computational and
robustness difficulties. <p>