Uncertainty-Aware Design Criteria for the Classification of Sensor Data

The design of low-cost distributed real-time classifiers whose inputs are the physical data from the environment is an issue of major interest in the emerging technology of so-called smart sensors. When a classifier has to be implemented on low-power and low-cost platforms, a tradeoff between classification accuracy and implementation complexity must be pursued. Here, a multiobjective optimization approach will be introduced to jointly minimize both the classification error rate and the platform resource usage. Objective evaluation is then a critical issue because design decision making is based on that. In practice, objective estimation is usually affected by uncertainty, which has to be taken into account in the design process. Here, we will consider the uncertainty that originates from the reduced size of the manually classified (labeled) data sets, which form the sole source of information used to build a learning-from-examples classifier. Then, the design criteria that make direct use and even try to take advantage of such uncertainty will be proposed. The proposed approach is validated using both synthetic and real-world data sets.