• Title/Summary/Keyword: Support vector classifier

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Development of Fuzzy Support Vector Machine and Evaluation of Performance Using Ionosphere Radar Data (Fuzzy Twin Support Vector Machine 개발 및 전리층 레이더 데이터를 통한 성능 평가)

  • Cheon, Min-Kyu;Yoon, Chang-Yong;Kim, Eun-Tai;Park, Mig-Non
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.4
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    • pp.549-554
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    • 2008
  • Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.

Modifying linearly non-separable support vector machine binary classifier to account for the centroid mean vector

  • Mubarak Al-Shukeili;Ronald Wesonga
    • Communications for Statistical Applications and Methods
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    • v.30 no.3
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    • pp.245-258
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    • 2023
  • This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier yi ∈ {-1, 1}. The modification takes into account the position of each data item xi from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

Design of SVM-Based Polynomial Neural Networks Classifier Using Particle Swarm Optimization (입자군집 최적화를 이용한 SVM 기반 다항식 뉴럴 네트워크 분류기 설계)

  • Roh, Seok-Beom;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.8
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    • pp.1071-1079
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    • 2018
  • In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.

The Classification of Electrocardiograph Arrhythmia Patterns using Fuzzy Support Vector Machines

  • Lee, Soo-Yong;Ahn, Deok-Yong;Song, Mi-Hae;Lee, Kyoung-Joung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.11 no.3
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    • pp.204-210
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    • 2011
  • This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

A Multi-Class Classifier of Modified Convolution Neural Network by Dynamic Hyperplane of Support Vector Machine

  • Nur Suhailayani Suhaimi;Zalinda Othman;Mohd Ridzwan Yaakub
    • International Journal of Computer Science & Network Security
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    • v.23 no.11
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    • pp.21-31
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    • 2023
  • In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

A Hybrid SVM-HMM Method for Handwritten Numeral Recognition

  • Kim, Eui-Chan;Kim, Sang-Woo
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1032-1035
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    • 2003
  • The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

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A Support Vector Machine Based Voltage Stability Classifier (SVM 기반 전압안정도 분류 알고리즘)

  • Dosano, Rodel D.;Song, Hwa-Chang;Lee, Byong-Jun
    • Proceedings of the KIEE Conference
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    • 2007.07a
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    • pp.477-478
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    • 2007
  • This paper proposes a new concept of support vector machine (SVM) based voltage stability classifier using time-series phasor data. The classifier, based on a linear SVM, can provide very effective signals for identification of long-term voltage stability. In addition, the SVM output is applicable as an voltage stability indicator when an amount of corrective controls are performed just to make the system reach around at the maximum deliverable point.

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Black-Box Classifier Interpretation Using Decision Tree and Fuzzy Logic-Based Classifier Implementation

  • Lee, Hansoo;Kim, Sungshin
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.1
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    • pp.27-35
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    • 2016
  • Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

(A Question Type Classifier based on a Support Vector Machine for a Korean Question-Answering System) (한국어 질의응답시스템을 위한 지지 벡터기계 기반의 질의유형분류기)

  • 김학수;안영훈;서정연
    • Journal of KIISE:Software and Applications
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    • v.30 no.5_6
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    • pp.466-475
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    • 2003
  • To build an efficient Question-Answering (QA) system, a question type classifier is needed. It can classify user's queries into predefined categories regardless of the surface form of a question. In this paper, we propose a question type classifier using a Support Vector Machine (SVM). The question type classifier first extracts features like lexical forms, part of speech and semantic markers from a user's question. The system uses $X^2$ statistic to select important features. Selected features are represented as a vector. Finally, a SVM categorizes questions into predefined categories according to the extracted features. In the experiment, the proposed system accomplished 86.4% accuracy The system precisely classifies question type without using any rules like lexico-syntactic patterns. Therefore, the system is robust and easily portable to other domains.

Support Vector Machine (SVM) based Voltage Stability Classifier (Support Vector Machine (SVM) 기반 전압안정성 분류 알고리즘)

  • Dosano, Rodel D.;Song, Hwa-Chang;Lee, Byong-Jun
    • Proceedings of the KIEE Conference
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    • 2006.11a
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    • pp.36-39
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    • 2006
  • This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurement data. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of PMU for monitoring power system dynamics. The methodology for fast monitoring of the system is initiated locally which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper briefly describes the mathematical background of SVM, and explains the procedure for fast classification of voltage stability using the SVM algorithm. To illustrate the effectiveness of the classifier, this paper includes numerical examples with a 11-bus test system.

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