Name
Abstract | ||
|---|---|---|
The nested generalized exemplar theory accomplishes learning by storing objects in Euclidean n-space, as hyperrectangles. Classification of new data is performed by computing their distance to the nearest “generalized exemplar” or hyperrectangle. This learning method permits to combine the distance-based classification with the axis-parallel rectangle representation employed in most of the rule-learning systems. This contribution proposes the use of evolutionary algorithms to select the most influential hyperrectangles to obtain accurate and simple models in classification tasks. The proposal is compared with the most representative nearest hyperrectangle learning approaches and the results obtained show that the evolutionary proposal outperforms them in accuracy and requires storing a lower number of hyperrectangles. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISDA.2009.238 | ISDA |
Keywords | Field | DocType |
influential hyperrectangles,evolutionary proposal,storing object,generalized exemplar,axis-parallel rectangle representation,evolutionary algorithm,distance-based classification,nearest hyperrectangle selection,classification task,euclidean n-space,nested generalized exemplar theory,evolutionary algorithms,first approach,evolutionary computation,learning artificial intelligence,data mining,algorithm design and analysis,computational modeling,accuracy | Hyperrectangle,Algorithm design,Exemplar theory,Evolutionary algorithm,Pattern recognition,Computer science,Rectangle,Evolutionary computation,Artificial intelligence,Euclidean geometry,Data classification,Machine learning | Conference |
ISSN | Citations | PageRank |
2164-7143 | 6 | 0.50 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvador García | 1 | 4151 | 118.45 |
| Joaquín Derrac | 2 | 2552 | 64.42 |
| Julian Luengo | 3 | 2418 | 77.15 |
| Francisco Herrera | 4 | 27391 | 1168.49 |