Created by W.Langdon from gp-bibliography.bib Revision:1.8081
In this paper, we introduce a term-weighting scheme that has been developed incrementally using an evolutionary learn- ing approach. We analyse one such term-weighting function produced from the evolutionary approach by decomposing it into inductive query and document growth functions. Con- sequently, we show that it is consistent with a number of axioms previously postulated for term-weighting schemes. Interestingly, we show that a further constraint can be de- rived from the resultant scheme.
Finally, we empirically validate our analysis, and the newly developed constraint, by showing that the newly developed nonparametric term-weighting scheme can outperform BM25 and the pivoted document length normalisation scheme over many different query types and collections. We conclude that the scheme produced from the learning approach adds further evidence to the validity of the axioms.",
Genetic Programming entries for Ronan Cummins Colm O'Riordan