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.
Original language | English |
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Pages (from-to) | 58-73 |
Journal | Journal of Intelligent Learning Systems and Applications |
Volume | 7 |
Issue number | 2 |
DOIs | |
Publication status | Published - 26 May 2015 |
Keywords
- Ensemble Classification
- Diversity
- Training Data