Name
Abstract | ||
|---|---|---|
Prototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. Evolutionary algorithms have been used recently for prototype selection showing good results. However, due to the complexity of this problem when the size of the databases increases, the behaviour of evolutionary algorithms could deteriorate considerably because of a lack of convergence. This additional problem is known as the scaling up problem. Memetic algorithms are approaches for heuristic searches in optimization problems that combine a population-based algorithm with a local search. In this paper, we propose a model of memetic algorithm that incorporates an ad hoc local search specifically designed for optimizing the properties of prototype selection problem with the aim of tackling the scaling up problem. In order to check its performance, we have carried out an empirical study including a comparison between our proposal and previous evolutionary and non-evolutionary approaches studied in the literature. The results have been contrasted with the use of non-parametric statistical procedures and show that our approach outperforms previously studied methods, especially when the database scales up. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.patcog.2008.02.006 | Pattern Recognition |
Keywords | Field | DocType |
heuristic search,data mining,population-based algorithm,databases increase,memetic algorithms,additional problem,prototype selection problem,data reduction,evolutionary prototype selection,evolutionary algorithms,scaling up,memetic algorithm,optimization problem,nearest neighbour rule,evolutionary algorithm,prototype selection,local search,non parametric statistics,empirical study | Convergence (routing),Memetic algorithm,Population,Heuristic,Evolutionary algorithm,Evolutionary computation,Artificial intelligence,Local search (optimization),Optimization problem,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
41 | 8 | Pattern Recognition |
Citations | PageRank | References |
101 | 2.09 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvador García | 1 | 1219 | 34.57 |
| José Ramón Cano | 2 | 400 | 15.64 |
| Francisco Herrera | 3 | 27391 | 1168.49 |