Topic categorization based on collectives of term weighting methods for natural language call routing

Natural language call routing is an important data analysis problem which can be applied in different domains including airspace industry. This paper presents the investigation of collectives of term weighting methods for natural language call routing based on text classification. The main idea is that collectives of different term weighting methods can provide classification effectiveness improvement with the same classification algorithm. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. After that different combinations of term weighting methods were formed as collectives. Two approaches for the handling of the collectives were considered: the meta-classifier based on the rule induction and the majority vote procedure. The numerical experiments have shown that the best result is provided with the vote of all seven different term weighting methods. This combination provides a significant increasing of classification effectiveness in comparison with the most effective term weighting methods.