A supervised machine learning classification algorithm for research articles

ABSTRACT

The issue of the automatic classification of research articles into one or more fields of science is of primary importance for scientific databases and digital libraries. A sophisticated classification strategy renders searching more effective and assists the users in locating similar relevant items. Although the most publishing services require from the authors to categorize their articles themselves, there are still cases where older documents remain unclassified, or the taxonomy changes over time. In this work we attempt to address this interesting problem by introducing a machine learning algorithm which combines several parameters and meta-data of a research article. In particular, our model exploits the training set to correlate keywords, authors, co-authorship, and publishing journals to a number of labels of the taxonomy. In the sequel, it applies this information to classify the rest of the documents. The experiments we have conducted with a large dataset comprised of about 1,5 million articles, demonstrate that in this specific application, our model outperforms the AdaBoost.MH and SVM methods.

References

1. CiteSeerX. http://csxstatic.ist.psu.edu/about/data.
2. S. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at TREC-3. Third Text REtrieval Conference, Gaithersburg, USA, 1994.
3. S. Robertson, H. Zaragoza, and M. Taylor. Simple BM25 Extension to Multiple Weighted Fields. In Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 42--49, 2004.
4. Q. Lu, and L. Getoor Link-based Classification, Advanced Methods for Knowledge Discovery from Complex Data, 2005
5. F. Sebastiani. Machine Learning in Automated Text Categorization ACM computing surveys (CSUR), 2002 vol. 34, issue 1, pp. 1--47
6. Y. Yang. An evaluation of statistical approaches to text categorization Information Retrieval, 1999 vol. 1, issue 1, pp. 69--90
7. T. Joachims. Text categorization with support vector machines: Learning with many relevant features, Machine Learning: ECML 1998, pp. 137--142
8. Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization, In Proceedings of the International Conference on Machine Learning (ICML'97), 1997, pp. 412--420.
9. X. Qi and B. D. Davidson. Web page classification: Features and algorithms, ACM Computing Surveys (CSUR), 2009, vol. 41, issue 2, pp. 1--31
10. M. Bilgic, and G. M. Namata, and L. Getoor. Combining collective classification and link prediction, In Proceedings of Workshop on Mining Graphs and Complex Structures at the IEEE International Conference on Data Mining, 2007, pp. 381--386
11. L. Getoor and C. P. Diehl. Link mining: a survey, ACM SIGKDD Explorations Newsletter, 2005, vol. 7, issue 2, pp. 3--12
12. S. A. Macskassy and F. Provost. Classification in networked data: A toolkit and a univariate case study, The Journal of Machine Learning Research, 2007, vol. 8, pp. 935--983
13. N. Fuhr. A probabilistic model of dictionary-based automatic indexing. In Proceedings of RIAO-85, 1st International Conference "Recherche d'Information Assistee par Ordinateur", 1985, pp. 207--216.
14. T. Joachims. Transductive inference for text classification using support vector machines. In Proceedings of the International Conference on Machine Learning (ICML'99), 1999, pp. 200--209.
15. Zhu, J. and Rosset, S. and Zou, H. and Hastie, T. Multi-class adaboost. In Ann Arbor, 2006 vol. 1001, issue 1, pp. 1612--1631.

Index Terms

• Information systems

  • Information systems applications