Multiclass and Binary SVM Classification: Implications for Training and Classification Users Export

@article{citeulike:3047357, abstract = {Support vector machines (SVMs) have considerable potential for supervised classification analyses, but their binary nature has been a constraint on their use in remote sensing. This typically requires a multiclass analysis be broken down into a series of binary classifications, following either the one-against-one or one-against-all strategies. However, the binary SVM can be extended for a one-shot multiclass classification needing a single optimization operation. Here, an approach for one-shot multi- class classification of multispectral data was evaluated against approaches based on binary SVM for a set of five-class classifications. The one-shot multiclass classification was more accurate (92.00\%) than the approaches based on a series of binary classifications (89.22\% and 91.33\%). Additionally, the one-shot multi- class SVM had other advantages relative to the binary SVM-based approaches, notably the need to be optimized only once for the parameters C and 7 as opposed to five times for one-against-all and ten times for the one-against-one approach, respectively, and used fewer support vectors, 215 as compared to 243 and 246 for the binary based approaches. Similar trends were also apparent in results of analyses of a data set of larger dimensionality. It was also apparent that the conventional one-against-all strategy could not be guaranteed to yield a complete confusion matrix that can greatly limit the assessment and later use of a classification derived by that method.}, author = {Mathur, A. and Foody, G. M.}, booktitle = {Geoscience and Remote Sensing Letters, IEEE}, citeulike-article-id = {3047357}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/LGRS.2008.915597}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4460898}, doi = {10.1109/LGRS.2008.915597}, journal = {Geoscience and Remote Sensing Letters, IEEE}, keywords = {imageclassification, multiclass, svm}, number = {2}, pages = {241--245}, posted-at = {2008-07-28 04:48:44}, priority = {3}, title = {Multiclass and Binary SVM Classification: Implications for Training and Classification Users}, url = {http://dx.doi.org/10.1109/LGRS.2008.915597}, volume = {5}, year = {2008} }

TY - JOUR ID - citeulike:3047357 L3 - citeulike-article-id:3047357 N2 - Support vector machines (SVMs) have considerable potential for supervised classification analyses, but their binary nature has been a constraint on their use in remote sensing. This typically requires a multiclass analysis be broken down into a series of binary classifications, following either the one-against-one or one-against-all strategies. However, the binary SVM can be extended for a one-shot multiclass classification needing a single optimization operation. Here, an approach for one-shot multi- class classification of multispectral data was evaluated against approaches based on binary SVM for a set of five-class classifications. The one-shot multiclass classification was more accurate (92.00%) than the approaches based on a series of binary classifications (89.22% and 91.33%). Additionally, the one-shot multi- class SVM had other advantages relative to the binary SVM-based approaches, notably the need to be optimized only once for the parameters C and 7 as opposed to five times for one-against-all and ten times for the one-against-one approach, respectively, and used fewer support vectors, 215 as compared to 243 and 246 for the binary based approaches. Similar trends were also apparent in results of analyses of a data set of larger dimensionality. It was also apparent that the conventional one-against-all strategy could not be guaranteed to yield a complete confusion matrix that can greatly limit the assessment and later use of a classification derived by that method. IS - 2 JF - Geoscience and Remote Sensing Letters, IEEE EP - 245 TI - Multiclass and Binary SVM Classification: Implications for Training and Classification Users VL - 5 T2 - Geoscience and Remote Sensing Letters, IEEE SP - 241 KW - imageclassification KW - multiclass KW - svm AU - Mathur, A AU - Foody, GM PY - 2008/// UR - http://dx.doi.org/10.1109/LGRS.2008.915597 ER -