TITLE:
Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers
AUTHORS:
Andreas Lianos, Yanyan Yang
KEYWORDS:
Ensemble Classification, Diversity, Training Data
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.7 No.2,
May
26,
2015
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.