Classification Based on Combination of Kernel Density Estimators

Abstract. A new classification algorithm based on combination of kernel density estimators is introduced. The method combines the estimators with different bandwidths what can be interpreted as looking at the data with different "resolutions" which, in turn, potentially gives the algorithm an insight into the structure of the data. The bandwidths are adjusted automatically to decrease the classification error. Results of the experiments using benchmark data sets show promising performance of the proposed approach when compared to classical algorithms. Key words: kernel density estimation, classification, density estimators combination

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Mateusz Kobos, Jacek Mandziuk
• Keywords: Density Estimators, Density Estimators Combination, ICANN 2009, Kernel Density Estimators, Neural Networks