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Vector score alpha integration for classifier late fusion
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2020-05-25 , DOI: 10.1016/j.patrec.2020.05.014
Gonzalo Safont , Addisson Salazar , Luis Vergara

Alpha integration is a family of integrators that encompasses many classic fusion operators (e.g., mean, product, minimum, maximum) as particular cases. This paper proposes vector score integration (VSI), a new alpha integration method for late fusion of multiple classifiers considering the joint effect of all the classes of the multi-class problem. Theoretical derivations to optimize the parameters of VSI for achieving the minimum probability of error are provided. VSI was applied to two classification tasks using electroencephalographic signals. The first task was the automatic stage classification of a neuropsychological test performed by epileptic subjects and the second one was the classification of sleep stages from apnea patients. Four single classifiers (linear and quadratic discriminant analysis, naive Bayes, and random forest) and three competitive fusion methods were estimated for comparison: mean, majority voting, and separated score integration (SSI). SSI is based on alpha integration, but unlike the proposed method, it considers the scores from each class in isolation, not accounting for possible dependencies among scores corresponding to different classes. VSI was able to optimally combine the results from all the single classifiers, in terms of accuracy and kappa coefficient, and outperformed the results of the other fusion methods in both applications.



中文翻译:

矢量分数Alpha积分用于分类器后期融合

Alpha集成是一个集成器家族,在特定情况下包含许多经典的融合运算符(例如,均值,乘积,最小值,最大值)。本文提出了矢量分数积分(VSI),这是一种考虑多个类别的所有类别的联合效应的,用于多个分类器后期融合的新alpha积分方法。提供了优化VSI参数以实现最小错误概率的理论推导。使用脑电图信号将VSI应用于两个分类任务。第一项任务是对癫痫患者进行的神经心理学测试的自动阶段分类,第二项任务是对呼吸暂停患者的睡眠阶段进行分类。四个单一分类器(线性和二次判别分析,朴素贝叶斯,以及随机森林)和三种竞争性融合方法进行了比较:均值,多数投票和独立分数积分(SSI)。SSI基于alpha积分,但是与提出的方法不同,它仅考虑来自每个类别的分数,而不考虑对应于不同类别的分数之间可能的依赖性。VSI能够在准确性和kappa系数方面对所有单个分类器的结果进行最佳组合,并且在两种应用中均优于其他融合方法的结果。不考虑对应于不同类别的分数之间可能的依赖性。VSI能够在准确性和kappa系数方面对所有单个分类器的结果进行最佳组合,并且在两种应用中均优于其他融合方法的结果。不考虑对应于不同类别的分数之间可能的依赖性。VSI能够在准确性和kappa系数方面对所有单个分类器的结果进行最佳组合,并且在两种应用中均优于其他融合方法的结果。

更新日期:2020-05-25
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