提出一种新的模糊分类模型,该模型利用决策粗糙集指导模糊分类模型结构的设计.首先采用模糊C均值聚类算法对连续属性离散化同时对输入空间进行模糊划分;然后利用两步搜索策略计算离散化决策表的约简,删除冗余的条件属性;从约简后的决策...提出一种新的模糊分类模型,该模型利用决策粗糙集指导模糊分类模型结构的设计.首先采用模糊C均值聚类算法对连续属性离散化同时对输入空间进行模糊划分;然后利用两步搜索策略计算离散化决策表的约简,删除冗余的条件属性;从约简后的决策表中提取决策规则,再将决策规则转换成模糊分类规则,从而建立了模糊分类模型.模糊分类模型的规则物理含义明确、形式简化,并且不需要再采用学习算法调整模型的参数.最后利用UCI(university of California irvine)标准数据集与现有的一些分类算法进行了比较,仿真实验结果证明了本文提出的模型是有效的.展开更多
The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
Toward the problem of judgement of enterprise crisis degree, the paper adopts fuzzy classification and fuzzy recognition method, forms enterprise character objective function in fuzzy condition, and brings forward to ...Toward the problem of judgement of enterprise crisis degree, the paper adopts fuzzy classification and fuzzy recognition method, forms enterprise character objective function in fuzzy condition, and brings forward to solve approach of optimal fuzzy classification center matrix, optimal fuzzy recognition matrix and optimal index weight under different crisis degree. By using the method in enterprise crisis early-warning example, it can distinguish enterprise crisis degree effectively.展开更多
文摘提出一种新的模糊分类模型,该模型利用决策粗糙集指导模糊分类模型结构的设计.首先采用模糊C均值聚类算法对连续属性离散化同时对输入空间进行模糊划分;然后利用两步搜索策略计算离散化决策表的约简,删除冗余的条件属性;从约简后的决策表中提取决策规则,再将决策规则转换成模糊分类规则,从而建立了模糊分类模型.模糊分类模型的规则物理含义明确、形式简化,并且不需要再采用学习算法调整模型的参数.最后利用UCI(university of California irvine)标准数据集与现有的一些分类算法进行了比较,仿真实验结果证明了本文提出的模型是有效的.
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
基金The work is supported by National Natural Science Foundation of China （70372011） and Higher Education PH.D Discipline-spot Special Fund（20030006009）
文摘Toward the problem of judgement of enterprise crisis degree, the paper adopts fuzzy classification and fuzzy recognition method, forms enterprise character objective function in fuzzy condition, and brings forward to solve approach of optimal fuzzy classification center matrix, optimal fuzzy recognition matrix and optimal index weight under different crisis degree. By using the method in enterprise crisis early-warning example, it can distinguish enterprise crisis degree effectively.
