Multi-class Support Vector Machine (SVM) Classifiers -- An Application in Hypothyroid Detection and Classification

Chamasemani, Fereshteh Falah; Singh, Yashwant

doi:10.1109/bic-ta.2011.51

Cited by 121 publications

(51 citation statements)

References 10 publications

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“…One yper plane is the one aration, or margin, we choose the hyper it to the nearest data abels to instances by where the labels are veral elements. The so is to reduce the to multiple binary n methods for such ary classifiers which he labels and the rest every pair of classes of new instances for by a winner-takes-all er with the highest [20].…”

Section: Experimental Results Amentioning

confidence: 99%

Analysis of biologically inspired model for object recognition

Arivazhagan¹,

Shebiah²,

Sophia³

et al. 2013

2013 International Conference on Recent Trends in Information Technology (ICRTIT)

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Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. This paper presents a biologically inspired model which gives a promising solution to object categorization in color space. Here, the biologically inspired features were extracted by log-polar Gabor Transform, aided by maximum operation and convolution with Prototype patches based on the saliency of the image. The extracted features are classified by SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI) and the results are presented.

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Section: Experimental Results Amentioning

confidence: 99%

Analysis of biologically inspired model for object recognition

Arivazhagan¹,

Shebiah²,

Sophia³

et al. 2013

2013 International Conference on Recent Trends in Information Technology (ICRTIT)

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“…For example, if we have two instances for each of two keywords, a and b, assume that those features vectors are a1=(7.2,10.3), a2=(2.7,4.1), b1=(15.0,3.9), and b2=(6.5,9.1). For ranked measures, we used a1=(5,7), a2=(1,3), b1= (8,2), and b2= (4,6). After that, we computed H for each feature set in Table 16 and for Figure 3, we calculated T R 2 g /N g for each keyword group.…”

Section: Kruskal-wallis Results and Detailsmentioning

confidence: 99%

“…It is well known that OAA is more accurate than OAO in most cases when we use SVMs [8]. For our experiment, we decided to use OAO, yielding more reasonable computational cost for multi-class classification as other researchers have done [7,22,30,44].…”

Section: Multiclass Classificationmentioning

confidence: 99%

Fingerprinting Keywords in Search Queries over Tor

Hopper

2017

Proceedings on Privacy Enhancing Technologies

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Search engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the contents of queries is to query the search engine over an anonymous network such as Tor. In this paper, we study the extent to which Website Fingerprinting can be extended to fingerprint individual queries or keywords to web applications, a task we call Keyword Fingerprinting (KF). We show that by augmenting traffic analysis using a two-stage approach with new task-specific feature sets, a passive network adversary can in many cases defeat the use of Tor to protect search engine queries. We explore three popular search engines, Google, Bing, and Duckduckgo, and several machine learning techniques with various experimental scenarios. Our experimental results show that KF can identify Google queries containing one of 300 targeted keywords with recall of 80% and precision of 91%, while identifying the specific monitored keyword among 300 search keywords with accuracy 48%. We also further investigate the factors that contribute to keyword fingerprintability to understand how search engines and users might protect against KF.

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“…05016-p. 6 Classification results of other common support vector classifiers with optimized parameters in dealing with multiple faults classification problems are provided in Table 4. Compared to Grid search-SVM, PSO-SVM and traditional TLBO-SVM, the classification accuracy of WETLBO-SVM for fault 4, fault 9 and fault 11 reaches 94%.…”

Section: Web Of Conferences Matecmentioning

confidence: 99%

“…SVM is widely used in fault diagnosis on account of its excellent processing capability of small sample problems [3][4][5] . Accuracy and performance of SVM depend on two parameters [6][7] , that is, the penalty parameter C and the kernel function parameter g. In order to find the optimal parameter accurately, the swarm intelligence optimization algorithm is normally adopted to carry out the parameter optimization during the training process of SVM. The accuracy of the swarm intelligence algorithm influences the performance of SVM to a large extent.…”

Section: Introductionmentioning

confidence: 99%

A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

Cao

Luo

2015

MATEC Web of Conferences

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Teaching-Learning-Based Optimization (TLBO) is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

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Multi-class Support Vector Machine (SVM) Classifiers -- An Application in Hypothyroid Detection and Classification

Cited by 121 publications

References 10 publications

Analysis of biologically inspired model for object recognition

Analysis of biologically inspired model for object recognition

Fingerprinting Keywords in Search Queries over Tor

A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

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