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首页> 外文会议>IEEE International Conference on Fuzzy Systems >A first approach towards the usage of classifiers’ performance to create fuzzy measures for ensembles of classifiers: a case study on highly imbalanced datasets
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A first approach towards the usage of classifiers’ performance to create fuzzy measures for ensembles of classifiers: a case study on highly imbalanced datasets

机译：利用分类器性能为分类器集合创建模糊度量的第一种方法：以高度不平衡的数据集为例

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In this work we study the possibility of learning fuzzy measures from classifiers' performance for improving the standard aggregation methods in classifier ensembles. Fuzzy measures are set-valued functions, which are not necessarily additive, and they are the basis for constructing non-linear fuzzy integrals, such as Choquet or Sugeno integral. These integrals have shown to be very useful in the aggregation of interacting criteria, since this interaction can be well modeled by a fuzzy measure. Classifier ensembles are composed of several classifiers and are aimed at improving the performance of every one of their counterparts. There are two main aspects about ensembles, first, how to build them, and second, how to combine the outputs of all their members. In this work, we focus on the second part, which is a key factor to obtain a successful ensemble. More specifically, we focus on the usage of fuzzy measures for the aggregation phase aiming at taking into account the coalitions and interactions among the members of the ensemble. Our hypothesis is that taking such information into account can lead to better performance. Moreover, we propose to directly obtain the fuzzy measure from data by considering the performance of each subset of classifiers in the ensemble. This way, one needs not include any additional learning for the fuzzy measure that can easily lead to overfitting. In order to test the usefulness of the proposed fuzzy measure, we will consider a set of 33 highly imbalanced datasets and we will develop a complete experimental study comparing the proposed combination scheme with other approaches commonly considered in the literature.
机译：在这项工作中，我们研究了从分类器的性能中学习模糊度量的可能性，以改进分类器集成中的标准聚合方法。模糊测度是集值函数，不一定是可加的，它们是构造非线性模糊积分（如Choquet或Sugeno积分）的基础。这些积分在交互标准的汇总中非常有用，因为可以通过模糊度量很好地建模这种交互。分类器乐团由几个分类器组成，旨在提高每个对应器的性能。关于合奏有两个主要方面，首先是如何构建它们，其次是如何组合所有成员的输出。在这项工作中，我们专注于第二部分，这是获得成功的合奏的关键因素。更具体地讲，我们关注于聚集阶段的模糊度量的使用，旨在考虑集合成员之间的联盟和相互作用。我们的假设是，将此类信息考虑在内可以带来更好的性能。此外，我们建议通过考虑集合中每个分类器子集的性能，直接从数据中获取模糊测度。这样，就不需要对模糊量度进行任何额外的学习，而这很容易导致过度拟合。为了测试所提出的模糊测度的有效性，我们将考虑一组33个高度不平衡的数据集，并且将进行一项完整的实验研究，将所提出的组合方案与文献中通常考虑的其他方法进行比较。

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