A regression model based on the nearest centroid neighborhood
Scholar | Other documents of the author: García, Vicente; Sánchez Garreta, José Salvador; Marqués Marzal, Ana Isabel; Martínez Peláez, R.
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TitleA regression model based on the nearest centroid neighborhood
The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has ... [+]
The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has also been used successfully for regression problems where the purpose is to predict a continuous numeric label. However, some alternative neighborhood definitions, such as the surrounding neighborhood, have considered that the neighbors should fulfill not only the proximity property, but also a spatial location criterion. In this paper, we explore the use of the k-nearest centroid neighbor rule, which is based on the concept of surrounding neighborhood, for regression problems. Two support vector regression models were executed as reference. Experimentation over a wide collection of real-world data sets and using fifteen odd different values of k demonstrates that the regression algorithm based on the surrounding neighborhood significantly outperforms the traditional k-nearest neighborhood method and also a support vector regression model with a RBF kernel. [-]
Investigation projectPROMETEOII/2014/062; TIN2013-46522-P
Bibliographic citationGARCÍA, V., et al. A regression model based on the nearest centroid neighborhood. Pattern Analysis and Applications, 2018, p. 1-11.
© Springer-Verlag London Ltd., part of Springer Nature 2018 “This is a post-peer-review, pre-copyedit version of an article published in Pattern Analysis and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s10044-018-0706-3”