- author = "Ronan Cummins and Colm O'Riordan",
- title = "A Framework for the study of Evolved Term-Weighting Schemes in Information Retrieval",
- booktitle = "TIR-06 Text based Information Retrieval, Workshop. ECAI 2006",
- year = "2006",
- editor = "Benno Stein and Odej Kao",
- address = "Riva del Garda, Italy",
- month = "29 " # aug,
- keywords = "genetic algorithms, genetic programming, information retrieval, phenotype distance",
-
URL = "
http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsECAI2006-Workshop.pdf",
-
URL = "http://www-ai.upb.de/aisearch/tir-06/proceedings/cummins06-framework-for-the-study-evolved-term-weighting-schemes-IR.pdf",
-
abstract = "Evolutionary algorithms and, in particular, Genetic
Programming (GP) are increasingly being applied to the
problem of evolving term-weighting schemes in
Information Retrieval (IR). One fundamental problem
with the solutions generated by these stochastic
processes is that they are often difficult to analyse.
A number of questions regarding these evolved
term-weighting schemes remain unanswered. One
interesting question is; do different runs of the GP
process bring us to similar points in the solution
space?
This paper deals with determining a number of measures of the distance between the ranked lists (phenotype) returned by different term-weighting schemes. Using these distance measures, we develop trees that show the phenotypic distance between these termweighting schemes. This framework gives us a representation of where these evolved solutions lie in the solution space.
Finally, we evolve several global term-weighting schemes and show that this framework is indeed useful for determining the relative closeness of these schemes and for determining the expected performance on general test data.",
- notes = "TIR-06 http://www.aisearch.de/tir-06/",
