摘要翻译：
特征约简是一个重要的概念，用于降维以降低分类的计算复杂度和时间。目前已经提出了许多解决这一问题的方法，但几乎所有的方法都只是针对每个输入数据集给出一个固定的输出，有些方法不能满足分类的要求。本文提出了一种对输入数据集进行处理的方法，以提高各特征提取方法的准确率。首先，提出了一种新的概念，称为逐步消除类(DCG)，以提高类的可分性。然后，利用该方法对特征约简方法的输入数据集进行处理，使其输出的误分类错误率大大降低。另外，基于特征约简的自适应数据集，该方法具有很好的去噪效果。在结果部分，通过使用UCI的一些数据集，比较了两个条件（有进程和没有进程）来支持我们的想法。
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英文标题：
《Dispelling Classes Gradually to Improve Quality of Feature Reduction
  Approaches》
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作者：
Shervan Fekri Ershad and Sattar Hashemi
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最新提交年份：
2012
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分类信息：

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 reduction is an important concept which is used for reducing dimensions to decrease the computation complexity and time of classification. Since now many approaches have been proposed for solving this problem, but almost all of them just presented a fix output for each input dataset that some of them aren't satisfied cases for classification. In this we proposed an approach as processing input dataset to increase accuracy rate of each feature extraction methods. First of all, a new concept called dispelling classes gradually (DCG) is proposed to increase separability of classes based on their labels. Next, this method is used to process input dataset of the feature reduction approaches to decrease the misclassification error rate of their outputs more than when output is achieved without any processing. In addition our method has a good quality to collate with noise based on adapting dataset with feature reduction approaches. In the result part, two conditions (With process and without that) are compared to support our idea by using some of UCI datasets.
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PDF链接：
https://arxiv.org/pdf/1206.1458