Soriano Antonio, Vergara Luis, Ahmed Bouziane, Salazar Addisson
Instituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, 46530 Valencia, Spain
Department of Electrical Engineering, University of Mostaganem, 27000 Mostaganem, Algeria
Neural Comput. 2015 Sep;27(9):1983-2010. doi: 10.1162/NECO_a_00766. Epub 2015 Jul 10.
We present a new method for fusing scores corresponding to different detectors (two-hypotheses case). It is based on alpha integration, which we have adapted to the detection context. Three optimization methods are presented: least mean square error, maximization of the area under the ROC curve, and minimization of the probability of error. Gradient algorithms are proposed for the three methods. Different experiments with simulated and real data are included. Simulated data consider the two-detector case to illustrate the factors influencing alpha integration and demonstrate the improvements obtained by score fusion with respect to individual detector performance. Two real data cases have been considered. In the first, multimodal biometric data have been processed. This case is representative of scenarios in which the probability of detection is to be maximized for a given probability of false alarm. The second case is the automatic analysis of electroencephalogram and electrocardiogram records with the aim of reproducing the medical expert detections of arousal during sleeping. This case is representative of scenarios in which probability of error is to be minimized. The general superior performance of alpha integration verifies the interest of optimizing the fusing parameters.
我们提出了一种融合不同探测器对应分数的新方法（双假设情况）。它基于α积分，我们已将其应用于检测场景。提出了三种优化方法：最小均方误差、ROC曲线下面积最大化以及错误概率最小化。针对这三种方法提出了梯度算法。包含了使用模拟数据和真实数据的不同实验。模拟数据考虑双探测器情况以说明影响α积分的因素，并展示分数融合相对于单个探测器性能所获得的改进。考虑了两个真实数据案例。第一个案例中，对多模态生物特征数据进行了处理。这种情况代表了在给定误报概率下要使检测概率最大化的场景。第二个案例是对脑电图和心电图记录进行自动分析，目的是重现医学专家对睡眠中觉醒的检测。这种情况代表了要使错误概率最小化的场景。α积分总体上的优越性能验证了优化融合参数的意义。
