模糊分类关联规则(Fuzzy Classification Association Rules,FCAR)是一种特殊的模糊关联规则,挖掘FCAR对于构建基于规则的分类模型至关重要。传统关联规则挖掘算法挖掘FCAR时可能会包含较多冗余规则,并且在数据集类别不平衡时,挖掘到的...模糊分类关联规则(Fuzzy Classification Association Rules,FCAR)是一种特殊的模糊关联规则,挖掘FCAR对于构建基于规则的分类模型至关重要。传统关联规则挖掘算法挖掘FCAR时可能会包含较多冗余规则,并且在数据集类别不平衡时,挖掘到的小类规则的数量会急剧减少甚至降为0。为解决上述问题,提出了一种基于特征选择和模糊类支持度-模糊提升度框架(Fuzzy Category Support-Fuzzy Lift Framework,FCS-FLF)的FCAR挖掘算法FSFCS Based FCARMiner(Feature Selection and Fuzzy Category Support-Fuzzy Lift Framework Based FCAR-Miner),基于模糊隶属度矩阵迭代挖掘FCAR。在多个类别不平衡的数据集上的实验结果表明,相比其他算法FSFCS Based FCAR-Miner算法能够避免大量冗余规则的生成,同时也能适应数据类别不平衡的情况,不会出现各类规则数量相差悬殊的情况。展开更多
To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree...To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.展开更多
文摘模糊分类关联规则(Fuzzy Classification Association Rules,FCAR)是一种特殊的模糊关联规则,挖掘FCAR对于构建基于规则的分类模型至关重要。传统关联规则挖掘算法挖掘FCAR时可能会包含较多冗余规则,并且在数据集类别不平衡时,挖掘到的小类规则的数量会急剧减少甚至降为0。为解决上述问题,提出了一种基于特征选择和模糊类支持度-模糊提升度框架(Fuzzy Category Support-Fuzzy Lift Framework,FCS-FLF)的FCAR挖掘算法FSFCS Based FCARMiner(Feature Selection and Fuzzy Category Support-Fuzzy Lift Framework Based FCAR-Miner),基于模糊隶属度矩阵迭代挖掘FCAR。在多个类别不平衡的数据集上的实验结果表明,相比其他算法FSFCS Based FCAR-Miner算法能够避免大量冗余规则的生成,同时也能适应数据类别不平衡的情况,不会出现各类规则数量相差悬殊的情况。
基金The National Natural Science Foundation of China(No.60473045)the Technology Research Project of Hebei Province(No.05213573)the Research Plan of Education Office of Hebei Province(No.2004406)
文摘To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.
