• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2573-2575
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• 2003

In this paper, we discuss a fuzzy neural network classifier with immune algorithm. The fuzzy neural network classifier is constructed with the fuzzy classifier and the neural network classifier based on fuzzy rules. To maximize performance of classifier, the immune algorithm and the back propagation algorithm are used. For the generalized classification ability, the simulation results from the iris data demonstrate superiority of the proposed classifier in comparison with other classifier.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.913-916
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• 1998

In this paper, to provide the robustness of character recognition, we propose a recognition method using the dilated boundary curve feature which has the invariance characteristics for the shift, scale, and rotation changes of character pattern. And its some characteristics and effectieness are evaluated through the experiments for both the english alphabets and the numeral digits. The feature vector is represented by the fourier descriptor for a boundary curve of the dilated character pattern which is generated by the circular mask dilation method, and is used for a nearest neighbort classifier(NNC) or a nearest neighbor mean classifier(NNMC). These the processing time and the recognition rate, and take also the robustness of recognition for both some internal noise and partial corruption of an image pattern.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.646-650
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• 2014

In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.544-553
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• 2013

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.290-294
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• 1997

Chromosomes, as the genetic vehicles, provide the basic material for a large proportion of genetic investigations. The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, we propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We are employed three morphological feature parameters ; centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), as input in neural network by preprocessing twenty human chromosome images. The results of our experiments show that our TMANN classifier is much more useful in neural network learning and successful in chromosome classification than the other classification methods.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2037-2040
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• 2004

In this paper we introduce a new prototype based classifiers for overlapping data, where training pattern can be overlap on the feature space. The proposed classifier is based on the prototype from neural network classifier (NNC)[1] for overlap data. The method automatically chooses the initial center and two radiuses for each class. The center is used as a mean representative of training data for each class. The unclassified pattern is classified by measure distance from the class center. If the distance is in the lower (shorter radius) the unknown pattern has the high percentage of being in this class. If the distance is between the lower and upper (further radius), the pattern has the probability of being in this class or others. But if the distance is outside the upper, the pattern is not in this class. We borrow the words upper and lower from the rough set to represent the region of certainty [3]. The training algorithm to find number of cluster and their parameters (center, lower, upper) is presented. The clustering result is tested using patterns from Thai handwritten letter and the clustering result is very similar to human eyes clustering.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.34-45
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• 1996

The signal pattern recognition program which can perform signal acquisition and processing, the extraction and selection of features, the classifier design and the evaluation, is developed and applied to the classification of artificial defects in the weld zone of Austenitic STS304. The neural network classifier is compared with the linear discriminant function classifier and the empirical Bayesian classifier. The signal through a broadband sensor is compared with that through a resonance type sensor. In recognition rate, the neural network classifier is best, and the signal through a broadband sensor is better.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.120-126
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• 2016

In this study, we propose a method for effectively detecting and recognizing the face in image using RBFNNs pattern classifier and HCbCr-based skin color feature. Skin color detection is computationally rapid and is robust to pattern variation for face detection, however, the objects with similar colors can be mistakenly detected as face. Thus, in order to enhance the accuracy of the skin detection, we take into consideration the combination of the H and CbCr components jointly obtained from both HSI and YCbCr color space. Then, the exact location of the face is found from the candidate region of skin color by detecting the eyes through the Haar-like feature. Finally, the face recognition is performed by using the proposed FCM-based RBFNNs pattern classifier. We show the results as well as computer simulation experiments carried out by using the image database of Cambridge ICPR.