On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule
| Autor: | Jorge García-Gutiérrez, José C. Riquelme-Santos, Daniel Mateos-García |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC-254: Data Science and Big Data Lab |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Cognitive Neuroscience Neighbours weighting Feature weighting Pattern recognition 02 engineering and technology computer.software_genre Evolutionary computation Computer Science Applications Weighting ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining K nearest neighbour business Evolutionary Computation Classifier (UML) computer |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | This paper presents an evolutionary method for modifying the behaviour of the k-Nearest-Neighbour clas sifier (kNN) called Simultaneous Weighting of Attributes and Neighbours (SWAN). Unlike other weighting methods, SWAN presents the ability of adjusting the contribution of the neighbours and the significance of the features of the data. The optimization process focuses on the search of two real-valued vectors. One of them represents the votes of neighbours, and the other one represents the weight of each feature. The synergy between the two sets of weights found in the optimization process helps to improve significantly, the classification accuracy. The results on 35 datasets from the UCI repository suggest that SWAN statistically outperforms the other weighted kNN methods |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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