• Journal of Information Processing Systems
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• 제20권2호
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권7_8호
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• pp.744-751
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• 2003

본 논문에서는 최근 뛰어난 예측력으로 각광받는 최소제곱 Support Vector Machine(Least Square Support Vector Machine: LS-SVM)과 First Principle(FP)을 결합한 하이브리드 최소제곱ㆍSupport Vector Machine 모델, HLS-SVM(Hybrid Least Square-Super Vector Machine)을 제안한다. 제안한 모델인 하이브리드 최소제곱 Support Vector Machine을 기존의 방법인 하이브리드 신경망(Hybrid Neural Network:HNN), 비선형 칼만필터와 하이브리드 신경망을 결합한 HNN-EKF (Hybrid Neural Network with Extended Kalman Filter) 모델과 비교해 보았다. HLS-SVM 모델은 학습 및 validation 과정에서는 HNN-EKF와 근사한 성능을 보였고, HNN 보다는 우수한 결과를 보였고, 일반화 성능에서는 HNN-EKF에 비해 3배, HNN보다 100배정도 우수한 결과를 보였다.

• 한국IT서비스학회지
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• 제10권2호
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Journal of the Korean Data and Information Science Society
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• 제22권2호
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• pp.197-206
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• 2011

신호처리 관련 응용문제에서는 신호에서 실시간으로 발생하는 비정상적인 사건들을 탐지하는 것이 매우 중요하다. 이전에 알려져 있는 비정상 사건 탐지방법들은 신호에 대한 명확한 통계적인 모형을 가정하고, 비정상적인 신호들은 통계적인 모형의 가정 하에서 비정상적인 사건들로 해석한다. 탐지방법으로 최대우도와 베이즈 추정 이론이 많이 사용되고 있다. 그러나 앞에서 언급한 방법으로는 로버스트 하고 다루기 쉬운 모형을 추정한다는 것은 쉽지가 않다. 좀 더 로버스트한 모형을 추정할 수 있는 방법이 필요하다. 본 논문에서는 로버스트 하다고 알려져 있는 서포트 벡터 기계를 이용하여 온라인으로 비정상적인 신호를 탐지하는 방법을 제안한다.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• 교육녹색환경연구
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• 제11권3호
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• 한국지능시스템학회논문지
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• 제13권6호
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• pp.655-660
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• 2003

일반적으로 Support Vector Machine은 이진 분류 모형에 있어 우수한 성능을 보이지만 모델의 한계로 인하여 다중 패턴의 분류 문제에는 쉽게 적용하기가 어렵다. 본 논문에서는 이진 분류를 포함한 다중 레이블을 갖는 데이터의 정확한 패턴 분류를 위하여 Zhu가 제안한 Import Vector Machine에 커널 Bagging 전략을 적용하여 분류의 정확성을 향상시키기 위한 Import Vector Voting 모형을 제안한다. 이러한 Import Vector Voting 모형은 다수의 커널함수를 적용한 결과 중에서 가장 성능이 우수한 커널함수를 이용하여 최종 분류를 수행하기 위한 voting 전략으로 사용한다. 본 논문에서 제안하는 Import Vector Voting 모형은 이진 분류를 포함한 3개 이상의 다중 패턴 데이터에 대한 분류 문제에 있어 매우 정확한 분류 성능을 보임을 실험을 통해 입증한다.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.873-878
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• 2003

LS-SVM(Least-Squares Support Vector Machine) has been used as a promising method for regression as well as classification. Suykens et al.(2000) used only the magnitude of residuals to obtain SVs(Support Vectors). Suykens' method behaves well for homogeneous model. But in a heteroscedastic model, the method shows a poor behavior. The present paper proposes a new method to get SVs. The proposed method uses the variance of noise as well as the magnitude of residuals to obtain support vectors. Through the simulation study we justified excellence of our proposed method.