A hierarchical classifier (cascade) is proposed for target detection. In building an optimal cascade we considered three heuristics: (1) use of a frontier-following approximation, (2) controlling error rates, and (3) weighting. Simulations of synthetic data with various underlying distributions were carried out. We found that a weighting heuristic is optimal in terms of both computational complexity and error rates. We initiate a systematic comparison of several potential heuristics that can be utilized in building a hierarchical model. A range of discussions regarding the implications and the promises of cascade architecture as well as of techniques that can be integrated into this framework is provided. The optimum heuristic—weighting algorithms—was applied to an IR data set. It was found that these algorithms outperform some state-of-the-art approaches that utilize the same type of simple classifier.
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