Multiclass and binary SVM classification: Implications for training and classification users

Ajay Mathur

doi:10.1109/LGRS.2008.915597

Return

Multiclass and binary SVM classification: Implications for training and classification users

2008-04-01

262

PRE

Ajay Mathur *

Giles M. Foody

DOI：10.1109/LGRS.2008.915597

Original

Original request for help

Save

Abstract

En 中文

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 multiclass SVM had other advantages relative to the binary SVM-based approaches, notably the need to he optimized only once for the parameters C and gamma 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.

Keywords:

accuracy

binary and multiclass classification

confusion matrix

image classification

support vector machine (SVM)