• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.60-64
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• 2001

In this paper, a new learning methodology for Kernel Methods is suggested that results in a sparse representation of kernel space from the training patterns for classification problems. Among the traditional algorithms of linear discriminant function(perceptron, relaxation, LMS(least mean squared), pseudoinverse), this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epochs. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.271-274
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• 1997

This paper presents an adaptive quantization of image sequences using the Radial Basis Function Network(RBFN) which classifies interframe image blocks. The clssification algorithm consists of two steps. Blocks are classified into NA(No Activity), SA(Small Activity), VA(Verical Activity), and HA(Horizontal Activity) classes according to edges, image activity and AC anergy distribution. RBFN is trained using the classification results of the above algorithm, which are nonlinear classification features are acquired from the complexity and variability of difference blocks. Simulation result shows that the the adaptive quantization using the RBFN method produced better results better results than that of the sorting and MLP methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.441-447
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• 2001

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause tole inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.209-215
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• 2003

A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.107-113
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• 2003

A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.2
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• pp.101-108
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• 2001

본 논문에서는 배전선로에서 발생하는 여러 고장유형을 판별하기 위해서 적응형 퍼지추론 시스템을 적용하는 새로운 기법을 제시하였다. 배전선로의 고장과 고장유사현상 데이터를 추출하기 위해서 EMTP를 이용하여 RL부하, 아크로부하, 컨버터부하가 있는 배전계통을 구성하고 여러 형태의 고장과 고장유사현상에 대해 시뮬레이션을 하였다. 이를 통해 얻은 전류 파형으로부터 기본파성분, 영상분전류, 짝수 고조파성분의 합, 홍수 고조파성분의 합, 그리고 비정규 고조파성분의 합의 5개의 입력변수를 추출하고 학습을 통해서 각 입력변수의 소속함수의 소속도를 자동으로 결정하였다. 이 적응형 퍼지추론 시스템을 이용한 기법을 평가하기 위해서 학습시와 다른 고장상황을 모의하여 얻은 데이터와 실증시험 데이터를 이용하였다. 결과적으로 제안한 기법은 배전선로에서 발생하는 고장유형을 빠르고 정확하게 판별할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.2
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• pp.141-147
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• 2001

소프트웨어의 측정값에 근거하여 소프트웨어 품질에 관한 의사결정을 할 때, 동치관계의 요구조건인 추이적(transitive) 특성이 항상 만족되는 것은 아니다. 순환수(cyclomatic number)가 거의 비슷한 프로그램에서, 하나의 \"구조적인\" 프로그램 범주에 속하고 또 다른 하나는 \"비구조적인\" 프로그램 범주에 속한다고 명확히 분류할 수 있는가하는 점이다. 따라서, 본 연구에서는 동치관계보다는 허용적 관계를 만족하는 허용적 러프집합에 근거한 소프트웨어 분류기준을 제시하고자 한다. 분류기준을 생성하기 위한 실험 데이터 집합을 수집하고, 집합 내의 각 원소에 관한 허용적 클래스들을 생성한 후, 각 허용적 클래스들의 중심값을 클러스터링하여 분류기준을 생성한다. 생성된 분류기준을 또 다른 실험 집합에 적용하여 비교 분석한 결과 생성된 분류기준이 타당함을 보여준다.생성된 분류기준이 타당함을 보여준다.