Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers

• 作者: Andreas Lianos Yanyan Yang
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 医学
• 关键词: ENSEMBLE Classification DIVERSITY TRAINING Data
• 年，卷（期）: 2015,（2）
• 页码范围: 58-73
• 页数: 16页
• 分类号: R73

摘要

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.