Title :
Self-selective clustering of training data using the maximally-receptive classifier/regression bank
Author :
Balasubramanian, Ram ; El-Sharkawi, M.A. ; Marks, R.J., II ; Jung, Jae-Byung ; Miyamoto, R.T. ; Andersen, G.M. ; Eggen, C.J. ; Fox, W. L J
Author_Institution :
EE Dept., Univ. of Washington, Seattle, WA, USA
Abstract :
A common approach to pattern classification problems is to train a bank of layered perceptrons or other classifiers by clustering the input training data and training each classifier with just the data from a specific cluster. There is no provision in such an approach, however, to assure the component layered perceptron is well suited to learn the training data cluster it is assigned. An alternate method of training, herein proposed, lets a layered perceptron in a classifier bank choose the cluster of inputs it processes on the basis of the perceptron´s ability to successfully classify those inputs. During training, data is therefore processed only by the classifier in the bank that best classifies the data or, equivalently, to which the data is most receptive. This allows each classifier to learn a localized subset of data dictated by the classifier´s own classification ability. Once each classifier in the bank is trained, a separate independent cluster pointer is trained to recognize to which cluster an input test pattern belongs. The cluster pointer is used in the test mode to identify which classifier in the bank will best classify the problem. The approach, also applicable to regression type problems, is illustrated through application on a simulated Gaussian data set and an active sonar test estimation problem. In both cases, the maximally receptive classifer/regression bank significantly outperforms a single layered perceptron trained on the same data.
Keywords :
pattern clustering; perceptrons; regression analysis; Gaussian data set; active sonar test estimation problem; component layered perceptron; data clustering; input test pattern; maximally-receptive classifier-regression bank; pattern classification; self-selective clustering; Aquaculture; Cybernetics; Data mining; Pattern classification; Pattern recognition; Physics; Sonar applications; Testing; Training data; USA Councils; active sonar classification; classifier banks; clustering; domain expertise; pattern classification; regression;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346820