针对重现概念漂移检测中的概念表征和分类器选择问题,提出了一种适用于含重现概念漂移的数据流分类的算法——基于主要特征抽取的概念聚类和预测算法(Conceptual clustering and prediction through main feature extraction,MFCCP)。MF...针对重现概念漂移检测中的概念表征和分类器选择问题,提出了一种适用于含重现概念漂移的数据流分类的算法——基于主要特征抽取的概念聚类和预测算法(Conceptual clustering and prediction through main feature extraction,MFCCP)。MFCCP通过计算不同批次样本的主要特征及影响因子的差异度以识别重复出现的概念,为每个概念维持且及时更新一个分类器,并依据Hoeffding不等式选择最合适的分类器对当前样本集实施分类,以提高对概念漂移的反应能力。在3个数据集上的实验表明:MFCCP在含重现概念漂移的数据集上的分类准确率,对概念漂移的反应能力及对概念漂移检测的准确率均明显优于其他4种对比算法,且MFCCP也适用于对不含重现概念漂移的数据流进行分类。展开更多
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into...An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.展开更多
文摘针对重现概念漂移检测中的概念表征和分类器选择问题,提出了一种适用于含重现概念漂移的数据流分类的算法——基于主要特征抽取的概念聚类和预测算法(Conceptual clustering and prediction through main feature extraction,MFCCP)。MFCCP通过计算不同批次样本的主要特征及影响因子的差异度以识别重复出现的概念,为每个概念维持且及时更新一个分类器,并依据Hoeffding不等式选择最合适的分类器对当前样本集实施分类,以提高对概念漂移的反应能力。在3个数据集上的实验表明:MFCCP在含重现概念漂移的数据集上的分类准确率,对概念漂移的反应能力及对概念漂移检测的准确率均明显优于其他4种对比算法,且MFCCP也适用于对不含重现概念漂移的数据流进行分类。
基金the Hunan Natural Science Foundation(No. 09JJ3129)the Hunan Key Social Science Foundation (No. 09ZDB04)the Hunan Social Science Foundation (No. 08JD28)
文摘An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.
