Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016; Lisbon; Lisbon
Год издания: 2016
Ключевые слова: Self-adjusting genetic algorithm, Term weighting, text classification, Weighted voting
Аннотация: The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the combination of stop-word filtering and stemming and the feature transformation based on term belonging to classes were considered. k-NN and SVM-FML were used as classification algorithms. In the paper the idea of voting with different term weighting methods was proposed. The majority vote of seven considered term weighting methods provides significant improvement of classification effectiveness. After that the weighted voting based on optimization with self-adjusting genetic algorithm was investigated. The numerical results showed that weighted voting provides additional improvement of classification effectiveness. Especially significant improvement of the classification effectiveness is observed with the feature transformation based on term belonging to classes that reduces the dimensionality radically; the dimensionality equals number of classes. Therefore, it can be useful for real-time systems as natural language call routing.
Журнал: ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics
Выпуск журнала: 1
Номера страниц: 38-46
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