摘要翻译：
本文研究并实现了多分类器的分类精度与多样性之间的关系。该研究对于构建强大的、能够更好地概括的分类器至关重要。委员会内神经网络的参数是不同的，以诱导多样性；因此，结构多样性是本研究的重点。隐节点和激活函数是变化的参数。利用生态学中采用的Shannon和Simpson等多样性测度对多样性进行量化。采用遗传算法以精度为代价函数寻找最优集成。观察到的结果表明，结构多样性与准确性之间存在关系。研究表明，集合的分类精度随着多样性的增加而提高。分类准确率提高了3%-6%。
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英文标题：
《Relationship between Diversity and Perfomance of Multiple Classifiers
  for Decision Support》
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作者：
R. Musehane, F. Netshiongolwe, F.V. Nelwamondo, L. Masisi and T.
  Marwala
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最新提交年份：
2008
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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：
  The paper presents the investigation and implementation of the relationship between diversity and the performance of multiple classifiers on classification accuracy. The study is critical as to build classifiers that are strong and can generalize better. The parameters of the neural network within the committee were varied to induce diversity; hence structural diversity is the focus for this study. The hidden nodes and the activation function are the parameters that were varied. The diversity measures that were adopted from ecology such as Shannon and Simpson were used to quantify diversity. Genetic algorithm is used to find the optimal ensemble by using the accuracy as the cost function. The results observed shows that there is a relationship between structural diversity and accuracy. It is observed that the classification accuracy of an ensemble increases as the diversity increases. There was an increase of 3%-6% in the classification accuracy.
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PDF链接：
https://arxiv.org/pdf/0810.3865