Bayesian instance selection for the nearest neighbor rule

An efficient instance selection algorithm for k nearest neighbor regression

The k-Nearest Neighbor algorithm(kNN) is an algorithm that is very simple to understand for classification or regression. It is also a lazy algorithm that does not use the training data points to do any generalization, in other words, it keeps all the training data during the testing phase. Thus, the population size becomes a major concern for kNN, since large population size may result in slow...

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

Feature and instance selection are two effective data reduction processes which can be applied to classification tasks obtaining promising results. Although both processes are defined separately, it is possible to apply them simultaneously. This paper proposes an evolutionary model to perform feature and instance selection in nearest neighbor classification. It is based on cooperative coevoluti...

An Instance Selection Algorithm Based on Reverse Nearest Neighbor

Class Conditional Nearest Neighbor and Large Margin Instance Selection

The one nearest neighbor (1-NN) rule uses instance proximity followed by class labeling information for classifying new instances. This paper presents a framework for studying properties of the training set related to proximity and labeling information, in order to improve the performance of the 1-NN rule. To this aim, a so-called class conditional nearest neighbor (c.c.n.n.) relation is introd...

Nearest-Neighbor Classification Rule

In this slecture, basic principles of implementing nearest neighbor rule will be covered. The error related to the nearest neighbor rule will be discussed in detail including convergence, error rate, and error bound. Since the nearest neighbor rule relies on metric function between patterns, the properties of metrics will be studied in detail. Example of different metrics will be introduced wit...