Generating Weighted Fuzzy Rules from Training Data for Dealing with the Iris Data Classification Problem
Generating Weighted Fuzzy Rules from Training Data for Dealing with the Iris Data Classification Problem
复制标题
从训练数据生成加权模糊规则以处理虹膜数据分类问题
DOI：
10.6703/ijase.2006.4(1).41
复制
发表时间：
2006
影响因子：
--
通讯作者：
Shyi
中科院分区：
文献类型：
--
作者：
Yung;Li;Shyi
模糊分类系统设计中最重要的任务是从训练数据中找到一组模糊规则来处理特定的分类问题。在本文中，我们提出了一种从训练数据生成加权模糊规则来处理鸢尾花数据分类问题的新方法。首先，我们将训练数据转换为模糊规则，然后合并这些模糊规则以减少模糊规则的数量。然后，我们通过输入变量的关系计算出现在生成的模糊规则中的每个输入变量的权重。所提出的加权模糊规则生成方法比现有方法获得了更高的平均分类准确率。
The most important task in the design of fuzzy classification systems is to find a set of fuzzy rules from training data to deal with a specific classification problem. In this paper, we present a new method to generate weighted fuzzy rules from training data to deal with the Iris data classification problem. First, we convert the training data to fuzzy rules, and then we merge those fuzzy rules in order to reduce the number of fuzzy rules. Then, we calculate the weight of each input variable appearing in the generated fuzzy rules by the relationships of input variables. The proposed weighted fuzzy rules generation method gets a higher average classification accu- racy rate than the existing methods.