  In this paper, we develop a decision support system for the hierarchical text
classification. We consider text collections with a fixed hierarchical
structure of topics given by experts in the form of a tree. The system sorts
the topics by relevance to a given document. The experts choose one of the most
relevant topics to finish the classification. We propose a weighted
hierarchical similarity function to calculate topic relevance. The function
calculates the similarity of a document and a tree branch. The weights in this
function determine word importance. We use the entropy of words to estimate the
weights.
  The proposed hierarchical similarity function formulates a joint hierarchical
thematic classification probability model of the document topics, parameters,
and hyperparameters. The variational Bayesian inference gives a closed-form EM
algorithm. The EM algorithm estimates the parameters and calculates the
probability of a topic for a given document. Compared to hierarchical
multiclass SVM, hierarchical PLSA with adaptive regularization, and
hierarchical naive Bayes, the weighted hierarchical similarity function has
better improvement in ranking accuracy in an abstract collection of a major
conference EURO and a website collection of industrial companies.
