Experimental evaluation of performance improvements in abductive network classifiers with problem decomposition

Problem decomposition and divide-and-conquer strategies have been proposed to improve the performance and realization of neural network solutions for complex problems. This paper reports on an experimental evaluation of performance gains brought about by problem decomposition for abductive network classifiers that classify four noisy waveform patterns having two waveform types (sine/cosine) and two different frequencies. Two-stage problem decomposition improves overall classification accuracy from 87.2% to 99%. Problem decomposition classifiers were found to be much more tolerant to model simplification and reduction in the training set size compared to monolithic solutions. This allows trading-off some of the large gain in classification performance for some other advantages that may be quite desirable in some applications, such as simpler models that execute faster and are easier to implement, smaller training sets, and shorter training times. A problem decomposition classifier is more accurate than a monolithic classifier in spite of the former being five times simpler, executing over two times faster, requiring one-fifth of the training data, and synthesized in one-eleventh of the training time. Performance is comparable with a neural network solution using the same decomposition method and significantly superior to an abductive network committee approach.