用实例+集成学习法，高效分类科研论文至研究领域。
An Instance-based Plus Ensemble Learning Method for Classification of Scientific Papers
- 以典型论文为种子，比对内容与引用特征
- 在DBLP数据集上分类准确率显著提升
- 适合需要自动归类大量论文的研究者
近年来科学出版物的指数级增长给有效高效的分类带来了重大挑战。本文提出一种结合实例学习与集成学习的新方法，将科研论文分类到相关研究领域。在设定一组研究领域后，首先人工分配若干典型种子论文至各领域；随后对每篇待分类论文，分别与各领域所有种子论文比对内容和引用信息；最后采用集成方法做出最终判断。基于DBLP数据集的实验表明，该方法在分类各类研究领域方面有效且高效。研究还发现，内容与引用特征均对论文分类具有价值。
原文摘要 · Abstract (English)
The exponential growth of scientific publications in recent years has posed a significant challenge in effective and efficient categorization. This paper introduces a novel approach that combines instance-based learning and ensemble learning techniques for classifying scientific papers into relevant research fields. Working with a classification system with a group of research fields, first a number of typical seed papers are allocated to each of the fields manually. Then for each paper that needs to be classified, we compare it with all the seed papers in every field. Contents and citations are considered separately. An ensemble-based method is then employed to make the final decision. Experimenting with the datasets from DBLP, our experimental results demonstrate that the proposed classification method is effective and efficient in categorizing papers into various research areas. We also find that both content and citation features are useful for the classification of scientific papers.
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