Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers
Andreas Lianos, Yanyan Yang · Journal of Intelligent Learning Systems and Applications · 2015
Typical supervised classification techniques require training instances similar to the values that need to be classified. This research proposes a methodology that can utilize training instances found in a different format. The benefit of this approach is that it allows the use of traditional classification techniques, without the need to hand-tag training instances if the information exists in other data sources. The proposed approach is presented through a practical classification application. The evaluation results show that the approach is viable, and that the segmentation of classifiers can greatly improve accuracy.