RT Journal Article
T1 Binary particle swarm optimization in classification
A1 Cervantes, Alejandro
A1 Galván, Inés M.
A1 Isasi, Pedro
AB Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of same well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), lo the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, lo compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.
PB Institute of Computer Science, Academy of Sciences of the Czech Republic
SN 1210-0552
YR 2005
FD 2005
LK https://hdl.handle.net/10016/4424
UL https://hdl.handle.net/10016/4424
LA eng
DS e-Archivo
RD 18 may. 2024