A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems

A comparison of decision trees with backpropagation neural networks for three distinct multi-modal problems: two from emitter classification and one from digit recognition, which shows that both methods produce comparable error rates but that direct application of either method will not necessarily produce the lowest error rate.

Abstract

Multi-modal classification problems involve the recognition of patterns where the patterns associated with each class can come from disjoint regions in feature space. Traditional linear discriminant methods cannot cope with these problems. While a number of approaches exist for classifying patterns with multiple modes, decision trees and backpropagation neural networks represent leading algorithms with special capabilities for dealing with this problem class. This paper provides a comparison of decision trees with backpropagation neural networks for three distinct multi-modal problems: two from emitter classification and one from digit recognition. These real-world problems provide an interesting range of problem characteristics for our comparison: one emitter classification problem has few features and a large data set; and the other has many features and a small data set. Additionally, both emitter classification problems have real-valued features, while the digit recognition problem has binary-valued features. The results show that both methods produce comparable error rates but that direct application of either method will not necessarily produce the lowest error rate. In particular, we improve decision tree results with multi-variable splits and we improve backpropagation neural networks with feature selection and mode identification.