Name
Title | ||
|---|---|---|
Accelerating a multi-objective memetic algorithm for feature selection using hierarchical k-means indexes. |
Abstract | ||
|---|---|---|
The (α, β)-k Feature Set Problem is a mathematical model proposed for multivariate feature selection. Unfortunately, addressing this problem requires a combinatorial search in a space that grows exponentially with the number of features. In this paper, we propose a novel index-based Memetic Algorithm for the Multi-objective (α, β)-k Feature Set Problem. The method is able to speed-up the search during the exploration of the neighborhood on the local search procedure. We evaluate our algorithm using six well-known microarray datasets. Our results show that exploiting the natural feature hierarchies of the data can have, in practice, a significant positive impact on both the solutions' quality and the algorithm's execution time.
|
Year | Venue | Field |
|---|---|---|
2018 | GECCO (Companion) | Memetic algorithm,Data mining,k-means clustering,Feature selection,Multivariate statistics,Computer science,Multi-objective optimization,Artificial intelligence,Local search (optimization),Combinatorial search,Machine learning,Exponential growth |
DocType | ISBN | Citations |
Conference | 978-1-4503-5764-7 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francia Jiménez | 1 | 0 | 0.68 |
| Claudio Sanhueza | 2 | 1 | 1.09 |
| Regina Berretta | 3 | 49 | 11.60 |
| Pablo Moscato | 4 | 334 | 37.27 |