• 한국정보통신학회논문지
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• 제19권1호
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• pp.35-40
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• 2015

본 논문은 유전 알고리즘 기반 귀납적 학습 환경 하에서 메타 학습법을 이용한 다중 분류기 시스템의 구축에 관한 것이다. 메타 학습법을 이용한 다중 분류기 시스템의 구축에서 분류기는 일반 분류기와 메타 분류기로 구성된다. 메타 분류기는 사례에 대한 일반 분류기의 분류 결과에 학습 알고리즘을 적용하여 얻어진다. 분류시스템의 의사 결정과정에서 메타 분류기의 역할은 일반 분류기의 분류 결과를 평가하여 최종 의사 결정 과정에의 참여 여부를 결정하는 것이다. 분류 시스템은 분류기의 분류 결과가 옳은 것으로 평가된 결과들만 취합하여 이를 바탕으로 최종 분류 결과를 도출해 낸다. 메타 학습법이 다중 분류기 시스템의 성능에 미치는 영향을 다수의 사례 집합을 이용하여 평가하였다.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.182-185
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• 2003

This paper presents a new design algorithm for the combination with the fuzzy classifier and the Bayesian classifier. Only few attempts have so far been made at providing an effective design algorithm combining the advantages and removing the disadvantages of two classifiers. Specifically, the suggested algorithms are composed of three steps: the combining, the fuzzy-set-based pruning, and the fuzzy set tuning. In the combining, the multi-inputs and multi-outputs (MIMO) fuzzy model is used to combine two classifiers. In the fuzzy-set-based pruning, to effectively decrease the complexity of the fuzzy-Bayesian classifier and the risk of the overfitting, the analysis method of the fuzzy set and the recursive pruning method are proposesd. In the fuzzy set tuning for the misclassified feature vectors, the premise parameters are adjusted by using the gradient decent algorithm. Finally, to show the feasibility and the validity of the proposed algorithm, a computer simulation is provided.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.109.3-109
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• 2002

1. Introduction 2. Vision system 3. Effect of brightness variations 4. Color classifier using multi-layer neural network 5. Experimental result of color classifier 6. Applications for robot soccer system 7. Conclusion

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.

• 한국전자통신학회논문지
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• 제5권3호
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• pp.315-320
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• 2010

다중 클래스로 이루어진 패턴을 분류하는 것은 데이터 베이스 시스템에서 기록을 그룹화하거나 VLSI 회로에서 어디에 결함이 있는지를 찾는 것 등에서 중요한 역할을 한다. 본 논문에서는 주어진 다중 클래스 패턴을 MACA(Multiple Attractor Cellular Automata)와 부분공간의 개념을 이용하여 가능한 최소 메모리량을 필요로 하는 다중 클래스 패턴 분류기를 구성하는 알고리즘을 제안한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.11-14
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• 1995

A Fuzzy Classifier which deals with very confusing objects is proposed. Naturally this classifier heavily relies on the nulti-feature decision-making procedure. For a simple example, this classifier is applied to the recognition of confusing handwritten numerals 4,6 and 9 The characteristic variables used in this paper are the existence of a loop and the relative location of the starting or ending points(SEP). Thus each sample of handwritten numerals 4, 6 and 9 is classified in one of the 6 groups which are divided according to the sample structure. Each group has its own classifying rules. Also the method of rule-generation using genetic algorithms in each group is proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.5087-5102
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• 2015

Rapid popularization of smart cell phone equipped with camera has led to a number of new legal and criminal problems related to multimedia such as digital image, which makes cell phone source identification an important branch of digital image forensics. This paper proposes a classifier combination based source identification strategy for cell phone images. To identify the outlier cell phone models of the training sets in multi-class classifier, a one-class classifier is orderly used in the framework. Feature vectors including color filter array (CFA) interpolation coefficients estimation and multi-feature fusion is employed to verify the effectiveness of the classifier combination strategy. Experimental results demonstrate that for different feature sets, our method presents high accuracy of source identification both for the cell phone in the training sets and the outliers.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.118-124
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• 2004

This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.