Intelligent fusion of structural and citation-based evidence for text classification

ABSTRACT

This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.

REFERENCES

• 1: Weiguo Fan , Michael D. Gordon , Praveen Pathak, Discovery of Context-Specific Ranking Functions for Effective Information Retrieval Using Genetic Programming, IEEE Transactions on Knowledge and Data Engineering, v.16 n.4, p.523-527, April 2004 [doi>10.1109/TKDE.2004.1269663]
• 2: John R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT Press, Cambridge, MA, 1992
• 3: Yiming Yang, Expert network: effective and efficient learning from human decisions in text categorization and retrieval, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, p.13-22, July 03-06, 1994, Dublin, Ireland

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
classification, document similarity, genetic programming