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Mutual Information Measures for Subclass Error-Correcting Output Codes Classification

机译：互信息措施，用于子类纠错输出代码分类

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摘要

Error-Correcting Output Codes (ECOCs) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclasses technique (sub-ECOC), where by splitting the initial classes of the problem we aim to the creation of larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a searching procedure known as sequential forward floating search (SFFS). The SFFS algorithm in each step searches for the optimum binary separation of the classes that compose the multi-class problem. The separation decision is based on the maximization or minimization of a criterion function. The standard criterion used is the maximization of the mutual information (MI) between the bi-partitions created in each step of the SFFS. The materialization of the MI measure is achieved by a method called fast quadratic Mutual Information (FQMI). Although FQMI is quite accurate in modelling the MI, its computation is of high algorithmic complexity, which as a consequence makes the ECOC and sub-ECOC techniques applicable only on small datasets. In this paper we present some alternative separation criteria of reduced computational complexity that can be used in the SFFS algorithm. Furthermore, we compare the performance of these criteria over several multi-class classification problems.
机译：纠错输出代码（ECOC）揭示了一种对多类分类问题进行建模的常用方法。根据这种最新技术，将多类问题分解为几个二进制问题。另外，在ECOC框架上，我们可以应用子类技术（sub-ECOC），其中通过拆分问题的初始类，我们旨在创建更大但更易于解决的ECOC配置。多类问题的分解是通过称为顺序前向浮动搜索（SFFS）的搜索过程实现的。 SFFS算法在每个步骤中搜索组成多类问题的类的最佳二进制分离。分离决策基于准则函数的最大化或最小化。使用的标准标准是在SFFS的每个步骤中创建的双向分区之间的互信息（MI）最大化。 MI度量的实现是通过一种称为快速二次互信息（FQMI）的方法实现的。尽管FQMI在建模MI方面非常准确，但其计算具有很高的算法复杂性，因此使ECOC和sub-ECOC技术仅适用于小型数据集。在本文中，我们提出了一些可以降低计算复杂度的替代标准，这些标准可以在SFFS算法中使用。此外，我们在几个多类分类问题上比较了这些标准的性能。

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