Functional data, which provides information about curves, surfaces or anything else varying over a continuum, has become commonly encountered type of data. The k-nearest neighbor (kNN) method, as nonparametric one the most popular supervised machine learning algorithms used to solve both classification and regression problems. This paper is devoted estimators functional model when observed vari...

This paper presents a new approach for solving binary quantification problems based on nearest neighbor (NN) algorithms. Our main objective is to study the behavior of these methods in the context of prevalence estimation. We seek for NN-based quantifiers able to provide competitive performance while balancing simplicity and effectiveness. We propose two simple weighting strategies, PWK and PWK...

Abstract The fuzzy k-nearest neighbor (FKNN) algorithm, one of the most well-known and effective supervised learning techniques, has often been used in data classification problems but rarely regression settings. This paper introduces a new, more general model. Generalization is based on usage Minkowski distance instead usual Euclidean distance. not optimal choice for practical problems, better...