N3 and BNN: Two New Similarity Based Classification Methods in Comparison with Other Classifiers.

Journal: Journal of chemical information and modeling
Published Date:

Abstract

Two novel classification methods, called N3 (N-nearest neighbors) and BNN (binned nearest neighbors), are proposed. Both methods are inspired by the principles of the K-nearest neighbors (KNN) method, being both based on object pairwise similarities. Their performance was evaluated in comparison with nine well-known classification methods. In order to obtain reliable statistics, several comparisons were performed using 32 different literature data sets, which differ for number of objects, variables and classes. Results highlighted that N3 on average behaves as the most efficient classification method with similar performance to support vector machine based on radial basis function kernel (SVM/RBF). The method BNN showed on average higher performance than the classical K-nearest neighbors method.

Authors

  • Roberto Todeschini
    Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 20126 Milan, Italy.
  • Davide Ballabio
    Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 20126 Milan, Italy.
  • Matteo Cassotti
    Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 20126 Milan, Italy.
  • Viviana Consonni
    Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca , P.zza della Scienza, 1, 20126 Milan, Italy.