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首页> 外文期刊>American journal of applied sciences >A FUZZY FAST CLASSIFICATION FOR SHARE MARKET DATABASE WITH LOWER AND UPPER BOUNDS
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A FUZZY FAST CLASSIFICATION FOR SHARE MARKET DATABASE WITH LOWER AND UPPER BOUNDS

机译：上下边界共享市场数据库的快速模糊分类

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摘要

In recent years, many researchers focused on the research topic of constructing fuzzy classification system. This study introduces a Fuzzy Fast Classification (FFC) approach for large data sets. It has three phases, in the first phase the large data base is reduced with the entropy by removing the number of attribute. In the second phase an approximate classification is obtained by the mean separation of the data by the total weight, upper and lower approximation line is drawn such that 20% of the record lies near the mean line. In the third phase the classification is refined by using fuzzy logic approach for the 20% of the record since they may fall in any one of the category which need to be carefully examined with the degree of fuzzy value. Experimental results for share market database demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. The proposed classifier has distinctive advantages on dealing with huge data sets.
机译：近年来，许多研究者将重点放在构建模糊分类系统的研究上。这项研究介绍了针对大型数据集的模糊快速分类（FFC）方法。它具有三个阶段，在第一阶段，通过删除属性的数量来减少大数据库的熵。在第二阶段中，通过将数据平均除以总权重来获得近似分类，并绘制上下近似线，以使记录的20％位于平均线附近。在第三阶段，通过使用模糊逻辑方法对记录的20％进行分类，因为它们可能属于需要仔细检查的模糊值程度的任何一个类别。股票市场数据库的实验结果表明，我们的方法具有良好的分类准确性，而训练速度明显快于其他SVM分类器。提出的分类器在处理海量数据集方面具有显着优势。

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