Conference proceeding
Intelligent fusion of structural and citation-based evidence for text classification
Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp.667-668
SIGIR '05
08/15/2005
DOI: 10.1145/1076034.1076181
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.
Details
- Title: Subtitle
- Intelligent fusion of structural and citation-based evidence for text classification
- Creators
- Baoping Zhang - Virginia TechYuxin Chen - Virginia TechWeiguo Fan - Virginia TechEdward Fox - Virginia TechMarcos Gonçalves - Universidade Federal de Minas GeraisMarco Cristo - Universidade Federal de Minas GeraisPàvel Calado - Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp.667-668
- Publisher
- ACM
- Series
- SIGIR '05
- DOI
- 10.1145/1076034.1076181
- Language
- English
- Date published
- 08/15/2005
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380491002771
Metrics
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