Survey on SVM and their application in image classification

Chandra, Mayank Arya; Bedi, S. S.

doi:10.1007/s41870-017-0080-1

Cited by 211 publications

(114 citation statements)

References 52 publications

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“…The performance of these feature selection methods is evaluated by examination of the classification accuracy and feature reduction rates. According to related literature, the SVM method can provide better classification performance than many other classification techniques in various pattern recognition problems (Byun & Lee, 2003; Chandra & Bedi, 2018; Huang et al, 2017; Mountrakis, Im, & Ogole, 2011; Reddy, Kavya, & Ramadevi, 2014). Therefore, the SVM method based on fivefold cross validation is used in the design of the classifier.…”

Section: Experimental Studiesmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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Feature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results. However, different feature selection algorithms use different criteria to select representative features, making it difficult to find the best algorithm for different domain datasets. The limitations of single feature selection methods can be overcome by the application of ensemble methods, combining multiple feature selection results. In the literature, feature selection algorithms are classified as filter, wrapper, or embedded techniques. However, to the best of our knowledge, there has been no study focusing on combining these three types of techniques to produce ensemble feature selection. Therefore, the aim here is to answer the question as to which combination of different types of feature selection algorithms offers the best performance for different types of medical data including categorical, numerical, and mixed data types. The experimental results show that a combination of filter (i.e., principal component analysis) and wrapper (i.e., genetic algorithms) techniques by the union method is a better choice, providing relatively high classification accuracy and a reasonably good feature reduction rate.

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Section: Experimental Studiesmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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“…The algorithms based on frequency can be realized at a lower computation cost by reducing the dimension of the frequency vectors [56]. SVM is sensitive to noises and outliers [57][58][59][60]. FSVM is based on fuzzy theory to reduce the influence of noises or outliers on the classification hyperplane [61][62][63][64][65][66].…”

Section: Previous Workmentioning

confidence: 99%

A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

Liu

2020

Applied Sciences

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Since SVM is sensitive to noises and outliers of system call sequence data. A new fuzzy support vector machine algorithm based on SVDD is presented in this paper. In our algorithm, the noises and outliers are identified by a hypersphere with minimum volume while containing the maximum of the samples. The definition of fuzzy membership is considered by not only the relation between a sample and hyperplane, but also relation between samples. For each sample inside the hypersphere, the fuzzy membership function is a linear function of the distance between the sample and the hyperplane. The greater the distance, the greater the weight coefficient. For each sample outside the hypersphere, the membership function is an exponential function of the distance between the sample and the hyperplane. The greater the distance, the smaller the weight coefficient. Compared with the traditional fuzzy membership definition based on the relation between a sample and its cluster center, our method effectively distinguishes the noises or outlies from support vectors and assigns them appropriate weight coefficients even though they are distributed on the boundary between the positive and the negative classes. The experiments show that the fuzzy support vector proposed in this paper is more robust than the support vector machine and fuzzy support vector machines based on the distance of a sample and its cluster center.

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“…In this part of study, the classification is a process where it determines the type of paddy leaf disease of the inserted paddy leaf image based on the extracted features beforehand. The SVM is implemented as it is a useful machine learning tool for classification [24]. It classifies the given data samples (in the form of vectors) by mapping them to high dimensional spaces and constructs hyper-planes that divide the data into partitions.…”

Section: Classificationmentioning

confidence: 99%

Automatic classification of paddy leaf disease

Ibrahim

Wahab

Fadzil

et al. 2019

IJEECS

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RiceisastaplefoodinmostoftheAsiancountries.Itisanimportantcrop, andoverhalfoftheworldpopulationreliesonitforfood.However,paddy leafdiseasecanaffectboththequalityandquantityofpaddyinagriculture production.Theclassificationofpaddyleafdiseaseisanimportantand urgenttaskasitdestroysabout10%to15%ofproductioninAsia.Thus,a studyonautomaticclassificationofpaddyleafdiseaseusingimage processingispresented.Featureextractiontechniquesofcolor,texture,and shapewereimplementedtoanalyzethecharacteristicsofthepaddyleaf disease.Inanother note,aSupportVector Machine(SVM)isused toclassify thefourtypesofpaddyleafdiseasewhicharethebrownspot,bacterialleaf blight,tungrovirus,andleaf scald.Theperformanceofthe proposedstudyis evaluatedto160testingimageswhichreturned86.25%ofclassification accuracy.Theoutcomeofthisstudyisexpectedtoassisttheagrotechnology industryinearlydetectionofpaddyleafdiseaseinwhichanappropriate actioncould be taken accordingly.

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Survey on SVM and their application in image classification

Cited by 211 publications

References 52 publications

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

Automatic classification of paddy leaf disease

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