Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers ()
ABSTRACT
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.
Share and Cite:
Lianos, A. and Yang, Y. (2015) Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers.
Journal of Intelligent Learning Systems and Applications,
7, 58-73. doi:
10.4236/jilsa.2015.72006.