Abstract
Information filtering has become an important component of modern information systems due to significant increase in its applica- tions. The objective of an information filtering is to classify/categorize documents as they arrive into the system. In this paper, we investigate an information filtering method based on steepest descent induction algorithm combined with a two-level preference relation on user ranking. The performance of the proposed algorithm is experimentally evaluated. The experiments are conducted using Reuters-21578 data collection. A micro-average breakeven effectiveness measure is used for performance evaluation. The best size of negative data employed in the training set is empirically determined and the effect of R norm factor on the learning process is evaluated. Finally, we demonstrate effectiveness of proposed method by comparing experimental results to other inductive methods.
This work is supported by the Army Research Office of USA, Grant No. DAAH04- 96-1-0325, under DEPSCoR program of Advanced Research Projects Agency, De- partment of Defense, and by University of Bahrain.
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Alsaffar, A.H., Deogun, J., Sever, H. (2000). Optimal Queries in Information Filtering. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_46
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DOI: https://doi.org/10.1007/3-540-39963-1_46
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