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摘要翻译：
提出了一种基于集成的数据丢失处理方法，该方法不需要预测或输入丢失值。该技术适用于神经网络的在线操作，可用于在线状态监测。该方法在分类和回归问题中都得到了验证。模糊ARTMAP集成用于分类，而多层感知器集成用于回归问题。将这种基于集成技术的结果与自联想神经网络和遗传算法相结合的结果进行了比较，结果表明这种方法在回归问题上的性能可以提高9%。提出的技术的另一个优点是它消除了寻找数据的最佳估计的需要，因此节省了时间。
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英文标题：
《Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs
with Missing Values》
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作者：
F.V. Nelwamondo and T. Marwala
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最新提交年份：
2007
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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：
An ensemble based approach for dealing with missing data, without predicting or imputing the missing values is proposed. This technique is suitable for online operations of neural networks and as a result, is used for online condition monitoring. The proposed technique is tested in both classification and regression problems. An ensemble of Fuzzy-ARTMAPs is used for classification whereas an ensemble of multi-layer perceptrons is used for the regression problem. Results obtained using this ensemble-based technique are compared to those obtained using a combination of auto-associative neural networks and genetic algorithms and findings show that this method can perform up to 9% better in regression problems. Another advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time.
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PDF链接：
https://arxiv.org/pdf/0705.1031
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