A classification algorithm based on geometric and statistical information

Classification is a long standing problem in computing. There are two broad kinds of classifiers, frequency based and geometry based. Frequency based classifiers often ignore the geometry underlying the data. Conversely, geometry based classifiers take into account the frequency only indirectly. This paper presents a classification algorithm which considers explicitly geometric and statistical characteristics of the data and combines them into a class representation. Reported here are initial experiments with this algorithm using two well known data sets, both with and without noise. The results show that the proposed algorithm is less sensitive to the training data set than other classifiers.