A granular agent evolutionary algorithm for classification
A granular agent evolutionary algorithm for classification
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DOI：
10.1016/j.asoc.2010.12.012
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发表时间：
2011-04
期刊：
影响因子：
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通讯作者：
Xiaoying Pan;L. Jiao
中科院分区：
文献类型：
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作者：
Xiaoying Pan;L. Jiao
受粒度进化算法的启发，提出了一种用于数据挖掘中分类任务的粒度代理进化分类（GAEC）算法。该方法使用粒度代理来表示具有相似属性的一些示例的集合，并且知识库指导粒度代理的演化。还设计了一些粒度进化算子来解决分类问题。同化算子、交换算子和微分算子分别反映了智能体的竞争能力、合作能力和自学习能力。最后，通过某种策略从粒度代理中提取一些分类规则来预测新数据的类别。实证研究包含UCI数据集、KDDCUP99数据集和远程图像识别。测试结果表明，该算法具有良好的分类预测能力，并且只需要付出较小的训练时间代价。在大多数UCI数据集中，GAEC的性能优于G-NET、OCEC和C4.5，它们都有不错的表现。同时，在这些UCI数据集中添加了一些高斯白噪声属性，结果表明GACE具有一定的抗噪声能力。为了测试 GAEC 的可扩展性，使用了两个维度上的两个函数：训练样本的数量和属性的数量。此外，GAEC还应用于一些现实世界的领域，入侵检测系统和遥感图像识别。 KDDCUP99的实验验证了GAEC具有处理现实世界中的海量数据的能力以及对未知类型数据的良好预测能力。最后，GAEC的准确率对于遥感图像识别也有很好的效果。
By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed. The method uses the granular agent to denote the set of some examples that have similar attributions and the knowledge base guides the evolution of granular agent. Also some granular evolutionary operators are designed for classification problem. Assimilation operator, exchange operator, and differentiation operator reflect the competitive, cooperative and self-learning ability of agent, respectively. Finally, some classification rules are extracted from granular agents by some strategy to forecast the sort of new data. Empirical study contains UCI data sets, KDDCUP99 data sets and remote image recognition. The test results show that the algorithm has a good classification prediction, and only need a small price for the training time. In most UCI data sets, the performance of GAEC is better than G-NET, OCEC and C4.5, which have good performance. At the same time, some Gaussian White Noise attributes are added to these UCI data sets and the results show GACE has some anti-noise abilities. To test the scalability of GAEC, two functions along two dimensions, the number of training examples and the number of attributes are used. Also, GAEC are applied to some real world fields, intrusion detection system and remote sensing image recognition. The experiments for KDDCUP99 verify GAEC has capability to deal with massive data in real world and good predicting capability for unknown type data. At last, the accuracy rate of GAEC is also good for the remote sensing image recognition.