FIRE-DES ++：用于动态包围圈选择的基分类器的增强在线修剪
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
摘要：
尽管在几个分类任务中非常有效，但动态包围选择（DES）技术可以选择将能力区域中的所有样本分类为来自同一类的分类器。Frienemy Indecision Region DES（FIRE-DES）通过预先选择分类器来解决这个问题，该分类器正确地将来自测试样本的能力区域中的不同类别的至少一对样本分类。然而，FIRE-DES应用预选的测试样本的分类，当且仅当其能力的区域是由来自不同类别的样本（indecision区域），即使这个标准是不可靠的，以确定是否测试样本位于接近类的边界（真正的indecision区域）时，使用经典的最近邻方法获得的能力的区域。正因为如此，FIRE-DES将噪声区域误认为是真正的犹豫不决区域，导致不合格分类器的预选，并将真正的犹豫不决区域误认为是安全区域，将样本留在这些区域而不进行任何预选。为了解决这些问题，我们提出了FIRE-DES++，一个增强的FIRE-DES，消除噪音，减少验证集中的类的重叠，并定义了使用相同数量的每个类的样本的竞争力的区域，避免选择一个区域的竞争力与一个单一的类的样本。实验使用FIRE-DES++与8种不同的动态选择技术上的64个分类数据集。实验结果表明，FIRE-DES++提高了所有DES技术的分类性能，在8种DES技术中有7种技术的性能优于FIRE-DES，并且优于最先进的DES框架。
英文摘要：
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correctly classify at least one pair of samples from different classes in the region of competence of the test sample. However, FIRE-DES applies the pre-selection for the classification of a test sample if and only if its region of competence is composed of samples from different classes (indecision region), even though this criterion is not reliable for determining if a test sample is located close to the borders of classes (true indecision region) when the region of competence is obtained using classical nearest neighbors approach. Because of that, FIRE-DES mistakes noisy regions for true indecision regions, leading to the pre-selection of incompetent classifiers, and mistakes true indecision regions for safe regions, leaving samples in such regions without any pre-selection. To tackle these issues, we propose the FIRE-DES++, an enhanced FIRE-DES that removes noise and reduces the overlap of classes in the validation set; and defines the region of competence using an equal number of samples of each class, avoiding selecting a region of competence with samples of a single class. Experiments are conducted using FIRE-DES++ with 8 different dynamic selection techniques on 64 classification datasets. Experimental results show that FIRE-DES++ increases the classification performance of all DES techniques considered in this work, outperforming FIRE-DES with 7 out of the 8 DES techniques, and outperforming state-of-the-art DES frameworks.
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