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吴波--Feature Selection via Cramer's V-Test Discretization for Remote-Sensing..

发布日期:2014-05-01 11:27:48 阅读次数:[2974]次 作者:

核心提示:吴波--Feature Selection via Cramer's V-Test Discretization for Remote-Sensing..


标题:Feature Selection via Cramer's V-Test Discretization for Remote-Sensing Image Classification
作者:Wu, B (Wu, Bo); Zhang, LP (Zhang, Liangpei); Zhao, YD (Zhao, Yindi)
来源出版物:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷:52 期:5 页:2593-2606 DOI:10.1109/TGRS.2013.2263510 出版年:MAY 2014
Web of Science 核心合集中的 "被引频次":0
被引频次合计:0
摘要:A feature selection method based on Cramer's V-test (CV-test) discretization is presented to improve the classification accuracy of remotely sensed imagery. Three possible contributions are pursued in this paper. First of all, a Cramer's V-based discretization (CVD) algorithm is proposed to optimally partition the continuous features into discrete ones. Two association-based feature selection indexes, the CVD-based association index (CVDAI) and the class-attribution interdependence maximization (CAIM)-based association index (CAIMAI), derived from the CV-test value, are then proposed to select the optimal feature subset. Finally, the benefit of using discretized features to improve the performance with the J48 decision tree (J48-DT) and naive Bayes (NB) classifiers is studied. To validate the proposed approaches, a high spatial resolution image and two hyperspectral data sets were used to evaluate the performances of CVD and the associated algorithms. The test performances of discretization using CVD and two other state-of -the-art methods, the CAIM and equal width, show that the CVD-based technique has the better ability to generate a good discretization scheme. Furthermore, the feature selection indexes, CVDAI and CAIMAI, perform better than the other used feature selection methods in terms of overall accuracies achieved by the J48-DT, NB, and support vector machine classifiers. Our tests also show that the use of discretized features benefits the J48-DT and NB classifiers.
地址:[Wu, Bo] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350002, Peoples R China.
[Zhang, Liangpei] Wuhan Univ, Key Lab Informat Engn Surveying Mapping & Remote, Wuhan 430079, Peoples R China.
[Zhao, Yindi] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221008, Peoples R China.
通讯作者地址:Wu, B (通讯作者),Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350002, Peoples R China.
电子邮件地址:wavelet778@sohu.com


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