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Lex Elisabeth, Granitzer Michael, Juffinger A., Seifert C.

Efficient Cross-Domain Classification of Weblogs

International Journal of Intelligent Computing Research (IJICR), Vol.1, Issue 2, Infonomics Society, 2010

Text classification is one of the core applicationsin data mining due to the huge amount ofuncategorized textual data available. Training a textclassifier results in a classification model that reflectsthe characteristics of the domain it was learned on.However, if no training data is available, labeled datafrom a related but different domain might be exploitedto perform cross-domain classification. In our work,we aim to accurately classify unlabeled weblogs intocommonly agreed upon newspaper categories usinglabeled data from the news domain. The labeled newsand the unlabeled blog corpus are highly dynamicand hourly growing with a topic drift, so theclassification needs to be efficient. Our approach is toapply a fast novel centroid-based text classificationalgorithm, the Class-Feature-Centroid Classifier(CFC), to perform efficient cross-domainclassification. Experiments showed that thisalgorithm achieves a comparable accuracy thank-Nearest Neighbour (k-NN) and Support VectorMachines (SVM), yet at linear time cost for trainingand classification. We investigate the classifierperformance and generalization ability using aspecial visualization of classifiers. The benefit of ourapproach is that the linear time complexity enables usto efficiently generate an accurate classifier,reflecting the topic drift, several times per day on ahuge dataset.
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