An analysis of how training data complexity affects the nearest neighbor classifiers

Authors:
J. S. Sánchez;R. A. Mollineda;J. M. Sotoca
Affiliations:
Universitat Jaume I, Departamento de Llenguatges i Sistemes Informátics, Av. Vicent Sos Baynat s/n, 12071, Castelló, Spain;Universitat Jaume I, Departamento de Llenguatges i Sistemes Informátics, Av. Vicent Sos Baynat s/n, 12071, Castelló, Spain;Universitat Jaume I, Departamento de Llenguatges i Sistemes Informátics, Av. Vicent Sos Baynat s/n, 12071, Castelló, Spain
Venue:
Pattern Analysis & Applications
Year:
2007

Citing 0
Cited 10

Error analysis in artificial neural networks: the imbalanced distribution case

SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method

Fuzzy Sets and Systems
Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study

IEEE Transactions on Evolutionary Computation
Shared domains of competence of approximate learning models using measures of separability of classes

Information Sciences: an International Journal
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

Pattern Recognition
Learner excellence biased by data set selection: A case for data characterisation and artificial data sets

Pattern Recognition
Analysis of data complexity measures for classification

Expert Systems with Applications: An International Journal
Towards UCI+: A mindful repository design

Information Sciences: an International Journal
Domains of competence of the semi-naive Bayesian network classifiers

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The k-nearest neighbors (k-NN) classifier is one of the most popular supervised classification methods. It is very simple, intuitive and accurate in a great variety of real-world domains. Nonetheless, despite its simplicity and effectiveness, practical use of this rule has been historically limited due to its high storage requirements and the computational costs involved. On the other hand, the performance of this classifier appears strongly sensitive to training data complexity. In this context, by means of several problem difficulty measures, we try to characterize the behavior of the k-NN rule when working under certain situations. More specifically, the present analysis focuses on the use of some data complexity measures to describe class overlapping, feature space dimensionality and class density, and discover their relation with the practical accuracy of this classifier.