2000
@inproceedings{PoH2000,
vgclass = {refpap},
author = {Pozo, Aurora R. and Hasse, Mozart},
title = {A Genetic Classifier Tool},
booktitle = {Proceedings of the 20th International Conference of the
Chilean Computer Science Society},
address = {Santiago, Chile},
pages = {14--23},
year = {2000},
url = {http://ieeexplore.ieee.org/iel5/7157/19262/00890387.pdf},
abstract = {Knowledge discovery is the most desirable end product of
an enterprise information system. Research from different areas
recognizes that a new generation of intelligent tools for automated
data mining is needed to deal with large databases. In this sense,
induction based learning systems have emerged as a promising approach.
This paper describes an induction-based classifier tool. The tool
employs a genetic algorithm using the Michigan approach to find rules,
is able to process discrete and continuous attributes and also is
domain-independent. Implementation details are explained, including
some optimizations, data structures and genetic operators. Some
optimizations include the use of phenotypic sharing (with linear
complexity) to direct the search. The results of accuracy are compared
with 33 other algorithms in 32 datasets. The difference of accuracy is
not statistically significant at the 10\% level when compared with the
best of the other 33 algorithms},
}