Minimizing response time in time series classification
Traditional cost-sensitive learning algorithms always deterministically predict examples as either positive or negative (in binary setting), to minimize the total misclassification cost. However, in more advanced real-world settings, the algorithms can also have another option to <i>reject</i> examples of high uncertainty. In this paper, we assume that cost-sensitive learning algorithms can reject the examples and obtain their true labels by paying <i>reject cost</i>. We therefore analyse three categories of popular cost-sensitive learning approaches, and provide generic methods to adapt them for reject option.