Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
A Framework for Titled Document Categorization with Modified Multinomial Naivebayes Classifier
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Classifying and ranking: the first step towards mining inside vertical search engines
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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In recent years, structured text documents like XML files are playing an important role in the Web-based applications. Among them, there are some documents that are segmented into different sections like “title”,“body”, etc. We call them “segmented documents”. To classify segmented documents, we can treat them as bags of words and use well-developed text classification models. However different sections in a segmented document may have different impact on the classification result. It is better to treat them differently in the classification process. Following this idea, two algorithms: IN_MIX and OUT_MIX are designed to label segmented documents by a trained classifier. We perform our algorithms using four frequently used models: SVM, NaiveBayes, Regression and Instance-based Classifiers. According to the experiment on Reuters-21578, the performance of different classification models is improved comparing to the conventional bag of words method.