Name
Title | ||
|---|---|---|
Meta-heuristic multi- and many-objective optimization techniques for solution of machine learning problems. |
Abstract | ||
|---|---|---|
Recently, multi- and many-objective meta-heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper-parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1111/exsy.12255 | EXPERT SYSTEMS |
Keywords | Field | DocType |
machine learning,meta-heuristic algorithms,multi-objective optimization | Online machine learning,Active learning (machine learning),Computer science,Meta heuristic,Multi-objective optimization,Artificial intelligence,Computational learning theory,Engineering optimization,Machine learning | Journal |
Volume | Issue | ISSN |
34.0 | 6.0 | 0266-4720 |
Citations | PageRank | References |
1 | 0.35 | 76 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Douglas Rodrigues | 1 | 76 | 5.12 |
| João P. Papa | 2 | 689 | 46.87 |
| Hojjat Adeli | 3 | 2150 | 148.37 |