Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution...Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.展开更多
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of hea...Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.展开更多
针对污秽绝缘子红外热像特征数据具有多重相关性的特点,提出基于PLS(Partial Least Squares,PLS)回归分析的高压绝缘子污秽等级判定方法。在最大限度保留原有数据信息的前提下,建立起高压绝缘子污秽特征量与污秽等级之间的PLS回归模型方...针对污秽绝缘子红外热像特征数据具有多重相关性的特点,提出基于PLS(Partial Least Squares,PLS)回归分析的高压绝缘子污秽等级判定方法。在最大限度保留原有数据信息的前提下,建立起高压绝缘子污秽特征量与污秽等级之间的PLS回归模型方程,通过对回归模型方程进行变量投影重要性指标分析,可以得到各个特征量对污秽等级判定结果的影响程度。此方法有效解决了自变量之间的多重相关性问题,量化了污秽特征量与污秽等级之间的关系。测试结果表明,将PLS回归分析应用于高压绝缘子污秽等级的判定,科学可靠,准确率高,具有较强的实用性。展开更多
基金This work was supported by the National High-tech Research and Development Program of China (No. 2003AA412110).
文摘Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.
基金the Hi-Tech Research and Development Program (863) of China (No. 2006AA10Z203)the National Scienceand Technology Task Force Project (No. 2006BAD10A01), China
文摘Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.
文摘针对污秽绝缘子红外热像特征数据具有多重相关性的特点,提出基于PLS(Partial Least Squares,PLS)回归分析的高压绝缘子污秽等级判定方法。在最大限度保留原有数据信息的前提下,建立起高压绝缘子污秽特征量与污秽等级之间的PLS回归模型方程,通过对回归模型方程进行变量投影重要性指标分析,可以得到各个特征量对污秽等级判定结果的影响程度。此方法有效解决了自变量之间的多重相关性问题,量化了污秽特征量与污秽等级之间的关系。测试结果表明,将PLS回归分析应用于高压绝缘子污秽等级的判定,科学可靠,准确率高,具有较强的实用性。
