Publicacións

@article{Rivero2007276,

title = {Automatic design of ANNs by means of GP for data mining tasks: Iris flower classification problem},

author = {Daniel Rivero and Juan R. Rabuñal-Dopico and Julián Dorado and Alejandro Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049057122&doi=10.1007%2f978-3-540-71618-1_31&partnerID=40&md5=5d24b505d810e5442f35a3058b0a7d81},

doi = {10.1007/978-3-540-71618-1_31},

issn = {03029743},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {4431 LNCS},

number = {PART 1},

pages = {276-285},

publisher = {Springer Verlag},

abstract = {This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a practical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Results show how this technique improves the results obtained with other techniques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional advantage that there has been a discrimination of the input variables. As it is explained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results. © Springer-Verlag Berlin Heidelberg 2007.},

note = {cited By 11},

keywords = {Automatic design; Iris flower, Classification (of information); Data mining; Genetic programming; Problem solving, Neural networks},

pubstate = {published},

tppubtype = {article}

}