International Conference on Cheminformatics and Computational Chemical Biology
Brisbane, Australia
Roberto Todeschini
University of Milano-Bicocca, Italy
Title: N3 and BNN: Two new similarity based classification methods. An extended comparison with other classifiers
Biography
Biography: Roberto Todeschini
Abstract
Two novel classification methods, called N3 (N-Nearest Neighbors) and BNN (Binned Nearest Neighbors), are proposed. Both methods are inspired to the principles of the K-Nearest Neighbors (KNN) method, both based on object pair-wise similarities. Their performance was evaluated in comparison with eight 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 slightly higher performance than the classical K-Nearest Neighbors method.