Multinomial logistic regression-based feature selection for hyperspectral data
Multinomial logistic regression-based feature selection for hyperspectral data
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DOI：
10.1016/j.jag.2011.09.014
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发表时间：
2012-02-01
影响因子：
7.5
通讯作者：
Pal, Mahesh
中科院分区：
文献类型：
--
作者：
Pal, Mahesh
本文评估了三种基于多项逻辑回归的特征选择方法的性能，并比较了最佳的基于多项逻辑回归的特征选择方法和基于支持向量机的递归特征消除方法的性能。使用了两个高光谱数据集，一个由65个特征组成（DAIS数据），另一个由185个特征组成（AVIRIS数据）。结果表明，通过使用Cawley和塔尔博特提出的基于多项式逻辑回归的特征选择方法选择的总共15到10个特征与使用DAIS和AVIRIS数据集的所有特征相比在分类准确性上实现了显著的提高。除了改进的性能之外，Cawley和塔尔博特方法不需要任何用户定义的参数，从而避免了模型选择阶段的要求。相比之下，其他两种基于多项式逻辑回归的特征选择方法需要一个用户定义的参数，并且在以下方面不如Cawley和塔尔博特方法表现得好：（i）实现与使用完整数据集实现的分类准确度相当的分类准确度所需的特征数量，以及（ii）由所选特征实现的分类准确度。Cawley和塔尔博特方法也被发现比SVM-RFE技术在计算上更有效，尽管两者都使用相同数量的选定特征来实现与全高光谱数据集相同甚至更高的精度水平。(C)2011 Elsevier B. V.保留所有权利。
This paper evaluates the performance of three feature selection methods based on multinomial logistic regression, and compares the performance of the best multinomial logistic regression-based feature selection approach with the support vector machine based recurring feature elimination approach. Two hyperspectral datasets, one consisting of 65 features (DAIS data) and other with 185 features (AVIRIS data) were used. Result suggests that a total of between 15 and 10 features selected by using the multinomial logistic regression-based feature selection approach as proposed by Cawley and Talbot achieve a significant improvement in classification accuracy in comparison to the use of all the features of the DAIS and AVIRIS datasets. In addition to the improved performance, the Cawley and Talbot approach does not require any user-defined parameter, thus avoiding the requirement of a model selection stage. In comparison, the other two multinomial logistic regression-based feature selection approaches require one user-defined parameter and do not perform as well as the Cawley and Talbot approach in terms of (i) the number of features required to achieve classification accuracy comparable to that achieved using the full dataset, and (ii) the classification accuracy achieved by the selected features. The Cawley and Talbot approach was also found to be computationally more efficient than the SVM-RFE technique, though both use the same number of selected features to achieve an equal or even higher level of accuracy than that achieved with full hyperspectral datasets. (C) 2011 Elsevier B.V. All rights reserved.