Toward Optimal Ordering of Prediction Tasks

Many applications involve a set of prediction tasks that must be accomplished sequentially through user interaction. If the tasks are interdependent, the order in which they are performed may have a significant impact on the overall performance of the prediction systems. However, manual specification of an optimal order may be difficult when the interdependencies are complex, especially if the number of tasks is large, making exhaustive search intractable. This paper presents the first attempt at solving the optimal task ordering problem using an approximate formulation in terms of pairwise task order preferences, reducing the problem to the well-known Linear Ordering Problem. We propose two approaches for inducing the pairwise task order preferences – 1) a classifier-agnostic approach based on conditional entropy that determines the prediction tasks whose correct labels lead to the least uncertainty for the remaining predictions, and 2) a classifier-dependent approach that empirically determines which tasks are favored before others for better predictive performance. We apply the proposed solutions to two practical applications that involve computer-assisted trouble report generation and document annotation, respectively. In both applications, the user fills up a series of fields and at each step, the system is expected to provide useful suggestions, which comprise the prediction (i.e. classification and ranking) tasks. Our experiments show encouraging improvements in predictive performance, as compared to approaches that do not take task dependencies into account.