摘要翻译：
特征选择是许多模式分类任务的重要预处理步骤。传统的特征选择方法都是为了获得一个能够导致高分类精度的特征子集而设计的。然而，最近在许多情况下，分类精度被证明是一个不合适的分类系统性能指标。相反，接收机工作特性曲线下面积(AUC)及其多级扩展MAUC被证明是更好的选择。因此，分类系统设计的目标逐渐从寻求具有最大分类精度的系统转向获得具有最大AUC/MAUC的系统。以往的研究表明，传统的特征选择方法需要改进以适应这一新的目标。这些方法通常只限于二元分类问题。针对多类分类问题，提出了一种基于MAUC分解的滤波器特征选择方法(MDFS)。据我们所知，MDFS是第一个专门为构建具有最大MAUC的分类系统而设计的特征选择方法。大量的实证结果表明MDFS与几种比较的特征选择方法相比具有优势。
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英文标题：
《Feature Selection for MAUC-Oriented Classification Systems》
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作者：
Rui Wang, Ke Tang
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Feature selection is an important pre-processing step for many pattern classification tasks. Traditionally, feature selection methods are designed to obtain a feature subset that can lead to high classification accuracy. However, classification accuracy has recently been shown to be an inappropriate performance metric of classification systems in many cases. Instead, the Area Under the receiver operating characteristic Curve (AUC) and its multi-class extension, MAUC, have been proved to be better alternatives. Hence, the target of classification system design is gradually shifting from seeking a system with the maximum classification accuracy to obtaining a system with the maximum AUC/MAUC. Previous investigations have shown that traditional feature selection methods need to be modified to cope with this new objective. These methods most often are restricted to binary classification problems only. In this study, a filter feature selection method, namely MAUC Decomposition based Feature Selection (MDFS), is proposed for multi-class classification problems. To the best of our knowledge, MDFS is the first method specifically designed to select features for building classification systems with maximum MAUC. Extensive empirical results demonstrate the advantage of MDFS over several compared feature selection methods.
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PDF链接：
https://arxiv.org/pdf/1105.2943