Nature and Society Relations Commons^™

Coupling Self-Organizing Maps With A Naïve Bayesian Classifier: Stream Classification Studies Using Multiple Assessment Data, Nikolaos Fytilis, Donna M. Rizzo

College of Engineering and Mathematical Sciences Faculty Publications

Organizing or clustering data into natural groups is one of the most fundamental aspects of understanding and mining information. The recent explosion in sensor networks and data storage associated with hydrological monitoring has created a huge potential for automating data analysis and classification of large, high-dimensional data sets. In this work, we develop a new classification tool that couples a Naïve Bayesian classifier with a neural network clustering algorithm (i.e., Kohonen Self-Organizing Map (SOM)). The combined Bayesian-SOM algorithm reduces classification error by leveraging the Bayesian's ability to accommodate parameter uncertainty with the SOM's ability to reduce high-dimensional data to lower …