Name
Abstract | ||
|---|---|---|
Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by under-sampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function. The results obtained show that a balancing of data performed by evolutionary under-sampling outperforms previously proposed under-sampling methods in classification accuracy, obtaining reduced subsets and getting a good balance on data. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11875581_168 | IDEAL |
Keywords | Field | DocType |
class imbalance problem,unbalanced data,evolutionary prototype selection algorithm,classification accuracy,reduced subsets,new fitness function,classification problem,good balance,data level,evolutionary under-sampling | Evolutionary algorithm,Computer science,Fitness function,Imbalance problems,Artificial intelligence,Machine learning,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
4224 | 0302-9743 | 3-540-45485-3 |
Citations | PageRank | References |
7 | 0.50 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvador García | 1 | 1219 | 34.57 |
| José Ramón Cano | 2 | 400 | 15.64 |
| Alberto Fernández | 3 | 1734 | 44.38 |
| Francisco Herrera | 4 | 27391 | 1168.49 |