Association rules enhanced classification of underwater acoustic signal

The classification of underwater acoustic signals is one of the important fields of pattern recognition. Inspired by the experience of training human experts in sonar, we propose a two-phase training algorithm to exploit association rules to reveal understandable intrinsic rules which contribute to correct classification in known mis-classification data sets. Preliminary experimental results demonstrate the potential of these classification association rules to enhance the classification accuracy of underwater acoustic signals