Efficient SVM training with reduced weighted samples

Nguyen, Giang; Phung, Son Lam; Bouzerdoum, A.

doi:10.1109/ijcnn.2010.5596745

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…In this way, if both sensitivity and specificity are high, the geometric mean G is also a high value; however, if one of the component accuracies-sensitivity or specificity-is low, the geometric mean G is affected by it. Thus, a classifier with high geometric mean is highly desirable for class-specific mapping [59], and hence G can be used to fine-tune binary algorithms of classification.…”

Section: Free-parameter Tuningmentioning

confidence: 99%

Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines

2017

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Abstract:In many remote sensing projects on land cover mapping, the interest is often in a sub-set of classes presented in the study area. Conventional multi-class classification may lead to a considerable training effort and to the underestimation of the classes of interest. On the other hand, one-class classifiers require much less training, but may overestimate the real extension of the class of interest. This paper illustrates the combined use of cost-sensitive and semi-supervised learning to overcome these difficulties. This method utilises a manually-collected set of pixels of the class of interest and a random sample of pixels, keeping the training effort low. Each data point is then weighted according to its distance to its near positive data point to inform the learning algorithm. The proposed approach was compared with a conventional multi-class classifier, a one-class classifier, and a semi-supervised classifier in the discrimination of high-mangrove in Saloum estuary, Senegal, from Landsat imagery. The derived classification accuracies were high: 93.90% for the multi-class supervised classifier, 90.75% for the semi-supervised classifier, 88.75% for the one-class classifier, and 93.75% for the proposed method. The results show that accuracy achieved with the proposed method is statistically non-inferior to that achieved with standard binary classification, requiring however much less training effort.

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Section: Free-parameter Tuningmentioning

confidence: 99%

Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines

2017

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“…others class) and to the degree in which the positive class (i.e. class of interest) is neglected (Nguyen et al 2010). …”

Section: Comparison and Evaluation Of Classifiersmentioning

confidence: 99%

Improving specific class mapping from remotely sensed data by cost-sensitive learning

Silva

Bação

Dieng

et al. 2017

International Journal of Remote Sensing

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In many remote sensing projects one is usually interested in a small number of land cover classes present in a study area and not in all the land cover classes that make-up the landscape. Previous studies in supervised classification of satellite images have tackled specific class mapping problem by isolating the classes of interest and combining all other classes into one large class, usually called others, and by developing a binary classifier to discriminate the class of interest from the others. Here, this approach is called focused approach. The strength of the focused approach is to decompose the original multi-class supervised classification problem into a binary classification problem, focusing the process on the discrimination of the class of interest. Previous studies have shown that this method is able to discriminate more accurately the classes of interest when compared with the standard multi-class supervised approach. However, it may be susceptible to data imbalance problems present in the training data set, since the classes of interest are often a small part of the training set. A result the classification may be biased towards the largest classes and, thus, be sub-optimal for the discrimination of the classes of interest. This study presents a way to minimise the effects of data imbalance problems in specific class mapping using cost-sensitive learning. In this approach errors committed in the minority class are treated as being costlier than errors committed in the majority class. Cost-sensitive approaches are typically implemented by weighting training data points accordingly to their importance to the analysis. By changing the weight of individual data points, it is possible to shift the weight from the larger classes to the smaller ones, balancing the data set. To illustrate the use of the cost-sensitive approach to map specific classes of interest, a series of experiments with weighted support vector machines classifier and Landsat Thematic Mapper data were conducted to discriminate two types of mangrove forest (high-mangrove and low-mangrove) in Saloum estuary, Senegal, a United Nations Educational, Scientific and Cultural Organisation World Heritage site. Results suggest an increase in overall classification accuracy with the use of cost-sensitive method (97.3%) over the standard multi-class (94.3%) and the focused approach (91.0%). In particular, cost-sensitive method yielded higher sensitivity and specificity values on the discrimination of the classes of interest when compared with the standard multi-class and focused approaches.

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“…The methods in [48]- [51], which are called "SVM via clustering," detect cluster centers using an unsupervised clustering method such as k-means clustering and leave only the cluster centers [48]- [50] or learning samples near the cluster centers [51]. In the case of [50], it gives weight to each remaining cluster center considering their cluster size to compensate for the information loss that might be caused by the learning sample removal. The methods in [52]- [54], which are called "reduced SVM (RSVM)," assume that randomly selected learning samples can represent all learning samples.…”

Section: Learning Sample Selection Of Prepruningmentioning

confidence: 99%

Support Vector Number Reduction: Survey and Experimental Evaluations

Jung

Kim

2014

IEEE Trans. Intell. Transport. Syst.

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Abstract-Although a support vector machine (SVM) is one of the most frequently used classifiers in the field of intelligent transportation systems and shows competitive performances in various problems, it has the disadvantage of requiring relatively large computations in the testing phase. To make up for this weakness, diverse methods have been researched to reduce the number of support vectors determining the computations in the testing phase. This paper is intended to help engineers using the SVM to easily apply support vector number reduction to their own particular problems by providing a state-of-the-art survey and quantitatively comparing three implementations belonging to postpruning, which exploits the result of a standard SVM. In particular, this paper confirms that the support vector number of a pedestrian classifier using a histogram-of-oriented-gradientbased feature and a radial-basis-function-kernel-based SVM can be reduced by more than 99.5% without any accuracy degradation using iterative preimage addition, which can be downloaded from the Internet.Index Terms-Reduced-set method, support vector machine (SVM), support vector number reduction (SVNR).

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Efficient SVM training with reduced weighted samples

Cited by 11 publications

References 21 publications

Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines

Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines

Improving specific class mapping from remotely sensed data by cost-sensitive learning

Support Vector Number Reduction: Survey and Experimental Evaluations

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