...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Soft Computing >An enhanced classification method comprising a genetic algorithm, rough set theory and a modified PBMF-index function
【24h】

An enhanced classification method comprising a genetic algorithm, rough set theory and a modified PBMF-index function

机译：一种改进的分类方法，包括遗传算法，粗糙集理论和改进的PBMF-index函数

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study proposes a method, designated as the GRP-index method, for the classification of continuous value datasets in which the instances do not provide any class information and may be imprecise and uncertain. The proposed method discretizes the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The proposed method consists of a genetic algorithm (GA) and an FRP-index method. In the FRP-index method, the conditional and decision attribute values of the instances in the dataset are fuzzified and discretized using the Fuzzy C-means (FCM) method in accordance with the cluster vectors given by the GA specifying the number of clusters per attribute. Rough set (RS) theory is then applied to determine the lower and upper approximate sets associated with each cluster of the decision attribute. The accuracy of approximation of each cluster of the decision attribute is then computed as the cardinality ratio of the lower approximate sets to the upper approximate sets. Finally, the centroids of the lower approximate sets associated with each cluster of the decision attribute are determined by computing the mean conditional and decision attribute values of all the instances within the corresponding sets. The cluster centroids and accuracy of approximation are then processed by a modified form of the PBMF-index function, designated as the RP-index function, in order to determine the optimality of the discretization/classification results. In the event that the termination criteria are not satisfied, the GA modifies the initial population of cluster vectors and the FCM, RS and RP-index function procedures are repeated. The entire process is repeated iteratively until the termination criteria are satisfied. The maximum value of the RP cluster validity index is then identified, and the corresponding cluster vector is taken as the optimal classification result. The validity of the proposed approach is confirmed by cross validation, and by comparing the classification results obtained for a typical stock market dataset with those obtained by non-supervised and pseudosupervised classification methods. The results show that the proposed GRP-index method not only has a better discretization performance than the considered methods, but also achieves a better accuracy of approximation, and therefore provides a more reliable basis for the extraction of decision-making rules.
机译：这项研究提出了一种称为GRP索引法的方法，用于对连续值数据集进行分类，其中实例不提供任何类别信息，并且可能不精确且不确定。所提出的方法离散化数据集中各个属性的值，并获得最佳聚类数和最佳分类精度。所提出的方法包括遗传算法（GA）和FRP指数方法。在FRP-index方法中，根据GA指定的聚类向量（指定每个属性的聚类数），使用Fuzzy C-均值（FCM）方法对数据集中实例的条件和决策属性值进行模糊化和离散化。然后应用粗糙集（RS）理论来确定与决策属性的每个聚类相关联的上下近似集。然后，将决策属性的每个聚类的近似准确度计算为较低近似集与较高近似集的基数比。最后，通过计算相应集合内所有实例的平均条件值和决策属性值，确定与决策属性的每个群集关联的较低近似集的质心。然后，通过修改形式的PBMF指标函数（称为RP指标函数）处理聚类质心和近似精度，以确定离散化/分类结果的最优性。如果不满足终止标准，则GA会修改簇向量的初始填充，并重复FCM，RS和RP-index函数过程。重复整个过程，直到满足终止条件为止。然后确定RP聚类有效性指标的最大值，并将相应的聚类向量作为最佳分类结果。通过交叉验证以及通过比较典型股票数据集获得的分类结果与通过非监督和伪监督分类方法获得的分类结果，可以验证所提出方法的有效性。结果表明，所提出的GRP指标方法不仅具有比所考虑的方法更好的离散性能，而且具有更好的近似精度，从而为决策规则的提取提供了更可靠的依据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号