An effective selection of retrieval schemes for data fusion
Keywords:Data fusion, filter, information retrieval, overlap, precision, student-t test.
Merging the results from more retrieval systems/schemes may enhance the performance of the Information Retrieval system. The success of the fusion lies in the selection of the member schemes. This paper explores an effective selection algorithm, which is derived from the filter concept, by treating low-score returning schemes as noises. The proposed algorithm is tested over the three benchmark test collections namely, American Documentation Institute (ADI), Centre for Inventions and Scientific Information (CISI), and Medlars (MED). The consistency of the computed result is tested by paired student-t test. It is observed that the presented algorithm results in significant improvement over the existing combination functions. The improvement in performance of the projected method is due to the reduction in amplification chorus effect caused by the low score returning schemes.
Batri K., MurugeshV., and Gopalan N P., “Effect of weight assignment in data fusion based information retrieval”, International Arab Journal of Information Technology, vol.8, no.3, pp. 244-250, 2011
Belkin N., Kantor P., and Quatrain R., “Combining evidence for information retrieval”, Proceedings of the 2nd Text REtieval Conference, pp. 35–44. 1994.
Bilhart H., “Learning retrieval expert combinations with genetic algorithm”, International Journal of Uncertainity,Fuzziness and Knowledge-Based Systems, vol 11, no 1, pp. 87–114, 2003.
Croft B., “Combining approaches to information retrieval”, W.B.Croft, ed., Advances in Information Retrieval, Kluwer Academic Publishers, chapter 1, pp. 1–36, 2003.
Fisher H L., and Elchesen D R., “Effectiveness of combining title words and index terms in machine retrieval searches”, Nature, vol 238, no 5359, pp.109–110, 1972.
Fox E A., and Shaw J A., “Combination of multiple searches”, Proceedings of the Second Text Retrieval Conference, pp. 243–252,1994.
Fox E A., and Shaw J A., “Combination of multiple searches”, Proceedings of the Third Text Retrieval Conference, pp. 105–108,1995.
Korfhage R R., “Information Storage and Retrieval”, Willey Computer Publishing, 1997.
Lee J H., “Combining multiple evidence from different properties of weighting schemes”, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 180–188,1995.
Lee J H.,” Analyses of multiple evidence combination”, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 260–276,1997.
Lee J H., “Combining multiple evidence from different relevant feedback networks”, Proceedings of the 5th international Conference on Database Systems for Advanced Applications, pp. 421–430,1997.
Salton G., and McGill M., “Introduction to Modern Information Retrieval”, McGraw–Gill, 1983.
Shanon C E., “Mathematical theory of communication”, The Bell System Technical journal, vol 27, pp. 379–423 and 626–656,1948.
Vogt C C.,” Adaptive Combination of Evidence for Information Retrieval”, PhD thesis, University of California, Sandiego, 1999.
Yates R B., and Neto B R., “Modern Information Retrieval”, Pearson Education,1999.
Zobel J., and Moffat A., “Exploring the similarity space”, ACM SIGIR Forum, vol 1, no 32, pp. 18–34,1998.