Fuzzy k-nearest neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise
| Autor: | Sheng-Tun Li, Francisco Herrera, Salvador García, Robert John, Sergio González |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Class (set theory) Mathematical optimization Degree (graph theory) Matching (graph theory) Computer science Cognitive Neuroscience Monotonic function 02 engineering and technology Fuzzy logic Computer Science Applications k-nearest neighbors algorithm Noise 020901 industrial engineering & automation Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | Neurocomputing. 439:106-121 |
| ISSN: | 0925-2312 |
| Popis: | This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do not comply with monotonic constraints due to class noise. MonFkNN incorporates a new calculation of fuzzy memberships, which increases robustness against monotonic noise without the need for relabeling. Our proposal has been designed to be adaptable to the different needs of the problem being tackled. In several experimental studies, we show significant improvements in accuracy while matching the best degree of monotonicity obtained by comparable methods. We also show that MonFkNN empirically achieves improved performance compared with Monotonic k-NN in the presence of large amounts of class noise. |
| Databáze: | OpenAIRE |
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