Binary tree optimization using genetic algorithm for multiclass support vector machine

Lee, Youngjoo; Lee, Jeongjin

doi:10.1016/j.eswa.2015.01.022

Cited by 21 publications

(12 citation statements)

References 22 publications

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“…To achieve M-ary classification using SVM, the most obvious approach is perform one-against-one comparison, or one-against-all training and testing. Another possible approach is known as direct acyclic graph SVM [11]. Alternatively, it is possible to develop multiclass optimization methodologies similar to the two-class case.…”

Section: Multiclass Scenariosmentioning

confidence: 98%

“…In this case multi-classes are clustered with maximum distance between clustering centers. Genetic algorithms were also used in [11] to develop a multi-class decision tree with binary SVM implemented at each node in the tree. In [12], an algorithm using the bio-inspired firefly algorithm was used for training and testing SVM for binary and multi-class scenarios.…”

Section: Multiclass Scenariosmentioning

confidence: 99%

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Radar target identification using probabilistic classification vector machines

Jouny

2016

SPIE Proceedings

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Radar target identification using probabilistic vector machines is investigated and tested using real radar data collected in a compact range for commercial aircraft models. Unlike relevance vector machines (RVM) that utilize zero-mean Gaussian prior for every weight for both negative and positive classes and are thus vulnerable to questionable (deceptive) vectors, probabilistic vector machines [2], alternatively, use nonnegative priors for the positive class and vice versa. This paper compares the performance of these machines with other target identification tools, and highlights scenarios where classification via a probabilistic vector machine is more plausible. The problem addressed in this paper is a M-ary target classification problem and is implemented as a set of pairwise comparisons between all competing hypotheses.

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Section: Multiclass Scenariosmentioning

confidence: 98%

Section: Multiclass Scenariosmentioning

confidence: 99%

Radar target identification using probabilistic classification vector machines

Jouny

2016

SPIE Proceedings

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“…x is the kernel function and k s , 1, 2, , k M are support vectors, which are determined during the training phase [16].…”

Section: Support Vector Machinementioning

confidence: 99%

Acoustics recognition of construction equipments based on LPCC features and SVM

Yang

Cao

Wang

2015

2015 34th Chinese Control Conference (CCC)

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To provide the reliability of power supply and ensure the personal security, cables in urban city are usually paved underground in nowadays. However, according to the statistical report from State Grid, around 53.4% of cable breakages are caused by external damages from 2009 to 2011. Among these external cable vandalisms, construction equipments are the main damage sources, including impact hammer, cutting machine, grab excavator, etc. Thus, designing a surveillance system which can automatically detect such potential destroy is highly desired. In this paper, we investigate the automatic recognition system based on classifying the acoustic waves generated by the three equipments. In the proposed recognition framework, the linear prediction cepstral coefficients (LPCC) for acoustic waves generated by the three equipments are extracted in small frames. These LPCC features are then fed to the support vector machine classifier for training and testing. To show the efficiency of the recognition system, real recorded acoustic waves are collected in varies construction using a cross microphone array for experiments. Experiments show that the recognition algorithm we developed is efficiency.

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“…Typical SVM only gives the two categories classification algorithm, but traffic information fusion needs to solve multi-classification problem. On the basis of above statements, the general method is to decompose the multi-classification problems into many binary-class problems, and the consolidation of many binary-class SVMs became the most popularly used method for multi-classification problems [15]. There are some commonly used multiclass classification methods based on SVM, e.g.…”

Section: Figure 1 -The Process Of Svm Classificationmentioning

confidence: 99%