Detecting activities from body-worn accelerometers via instance-based algorithms
Pervasive and Mobile Computing
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Most of classifiers implicitly assume that data samples belong to at least one class among predefined classes. How- ever, all data patterns may not be known at the time of data collection or a new pattern can be emerging over time. Hence ideal classifiers need to be able to recognize an emerging pattern. In this paper, we explore the per- formances and limitations of the existing classification sys- tems in detecting a new class. Also a new method is pro- posed that can monitor the change in class distribution and detect an emerging class. It works under the supervised learning model where along with classification an emerg- ing class with new characteristic is detected so that classi- fication model can be adapted systematically. For detection of an emerging class, we design statistical significance test- ing for signaling change of class distribution. When the alarm for new class generation is set on, candidates for new class members are retrieved for close examination by experts. Our experimental results demonstrate competent performances of the proposed method.