Strojno Ucenje Na Nehomogenih, Distribuiranih Tekstovnih Podatkih

This dissertation proposes new machine learning methods where the corresponding learning problem is characterized by a high number of features, unbalanced class distribution and asymmetric misclassification costs. The input is given as a set of text documents or their Web addresses (URLs). The induced target concept is appropriate for the classification of new documents including shortened documents describing individual hyperlinks. The proposed methods are based on several new solutions. Proposed is a new, enriched document representation that extends the bag-of-words representation by adding word sequences and document topic categories. Features that represent word sequences are generated using a new efficient procedure. Features giving topic categories are obtained from background knowledge constructed using the new machine learning method for learning from class hierarchies. When learning from class hierarchy, a high number of class values, examples and features, are handled by (1)..