Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is ...Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is hard to deploy in many real applications because of the large memory requirement and high computation cost to store and vote the predictions of component classifiers.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information,which has attracted a lot of attention from theory and application fields.In this paper,a novel rough sets based method is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation.Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.展开更多
针对传统支持向量机(Support Vector Machine,SVM)集成学习(Ensemble Learning,EL)方法不能够解决高维复杂数据且子学习器差异性小集成效果不明显的问题,提出一种基于多种特征选择方法进行Bagging集成的支持向量机学习(Support Vector M...针对传统支持向量机(Support Vector Machine,SVM)集成学习(Ensemble Learning,EL)方法不能够解决高维复杂数据且子学习器差异性小集成效果不明显的问题,提出一种基于多种特征选择方法进行Bagging集成的支持向量机学习(Support Vector M achine Based on M ultiple Feature Selection Bagging,M FSB_SVM)方法.该方法首先采用不同的特征选择方法构建子学习器,以增加不同子学习器间的差异性,并直接从训练数据中对样本特征的重要性进行评估,而无需学习算法的反馈.实验表明,本文提出的MFSB_SVM方法既可以有效解决高维数据问题,也可避免传统SVM集成方法效果不明显的缺点,从而进一步提高学习模型的泛化性能.展开更多
考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方...考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方法,实现了数据的随机抽样,分成不同组数据集进行相互独立的训练,避免对舞动数据过拟合,提升机器学习算法的抗噪声能力以及泛化能力,采用k折交叉验证算法进行模型的验证,并利用F1-score描述导线舞动预警模型的性能,验证了该方法在舞动预测方面的有效性。展开更多
基金Supported by the National Natural Science Foundation of China(Granted No.60775036 and No.60475019)the Ph.D.programs Foundation of Ministry of Education of China(No.20060247039)
文摘Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is hard to deploy in many real applications because of the large memory requirement and high computation cost to store and vote the predictions of component classifiers.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information,which has attracted a lot of attention from theory and application fields.In this paper,a novel rough sets based method is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation.Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.
文摘针对传统支持向量机(Support Vector Machine,SVM)集成学习(Ensemble Learning,EL)方法不能够解决高维复杂数据且子学习器差异性小集成效果不明显的问题,提出一种基于多种特征选择方法进行Bagging集成的支持向量机学习(Support Vector M achine Based on M ultiple Feature Selection Bagging,M FSB_SVM)方法.该方法首先采用不同的特征选择方法构建子学习器,以增加不同子学习器间的差异性,并直接从训练数据中对样本特征的重要性进行评估,而无需学习算法的反馈.实验表明,本文提出的MFSB_SVM方法既可以有效解决高维数据问题,也可避免传统SVM集成方法效果不明显的缺点,从而进一步提高学习模型的泛化性能.
文摘考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方法,实现了数据的随机抽样,分成不同组数据集进行相互独立的训练,避免对舞动数据过拟合,提升机器学习算法的抗噪声能力以及泛化能力,采用k折交叉验证算法进行模型的验证,并利用F1-score描述导线舞动预警模型的性能,验证了该方法在舞动预测方面的有效性。
