Label Alteration to Improve Underwater Mine Classification

A new algorithm for performing supervised classification that intentionally alters the training labels supplied with the data set is presented. The proposed approach is motivated by the insight that the average prediction of a group of sufficiently informed people is often more accurate than the prediction of any one supposed expert. This idea that the “wisdom of crowds” can outperform a single expert is implemented in two ways. When labeling error rates can be estimated, sets of labels are drawn as samples from a Bernoulli distribution. When side information is not available, or no labeling errors are suspected, labels are intentionally altered in a structured manner. The framework is demonstrated in the context of an underwater mine classification application on synthetic aperture sonar data collected at sea, with promising results.