- title = "Improving the learning of {Boolean} queries by means of a multiobjective IQBE evolutionary algorithm",
- author = "O. Cordon and E. Herrera-Viedma and M. Luque",
- journal = "Information Processing and Management",
- year = "2006",
- volume = "42",
- number = "3",
- pages = "615--632",
- month = may,
- keywords = "genetic algorithms, genetic programming, Boolean information retrieval systems, Inductive query by example, Multiobjective evolutionary algorithms, Query learning",
-
DOI = "
doi:10.1016/j.ipm.2005.02.006",
-
abstract = "The Inductive Query By Example (IQBE) paradigm allows
a system to automatically derive queries for a specific
Information Retrieval System (IRS). Classic IRSs based
on this paradigm [Smith, M., & Smith, M. (1997). The
use of genetic programming to build Boolean queries for
text retrieval through relevance feedback. Journal of
Information Science, 23(6), 423-431
\cite{MartinPSmith:1997:JIS}] generate a single
solution (Boolean query) in each run, that with the
best fitness value, which is usually based on a
weighted combination of the basic performance criteria,
precision and recall. A desirable aspect of IRSs,
especially of those based on the IQBE paradigm, is to
be able to get more than one query for the same
information needs, with high precision arid recall
values or with different trade-offs between both.
IQBE process is proposed combining a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van Veldhuizen, D. A., & Lamant, G. B. (2002). Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a different precision recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the well-known Smith and Smith's algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process.",
