• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1849-1857
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• 1999

We proposed the RPA (Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. This algorithm recursively partitions the pattern space until each hyperrectangle contains only those patterns of the same class, then it computes the average values of patterns in each hyperrectangle to extract a representative. Also we have used the mutual information between the features and classes as weights for features to improve the classification performance. The proposed algorithm used 30~90% of memory space that is needed in the k-NN (k-Nearest Neighbors) classifier, and showed a comparable classification performance to the k-NN. Also, by reducing the number of stored patterns, it showed an excellent result in terms of classification time when we compare it to the k-NN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.956-961
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• 2006

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1509-1518
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• 2017

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.110-114
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• 2010

We describe a new method for text categorization using TextRank algorithm. Text categorization is a problem that over one pre-defined categories are assigned to a text document. TextRank algorithm is a graph-based ranking algorithm. If we consider that each word is a vertex, and co-occurrence of two adjacent words is a edge, we can get a graph from a document. After that, we find important words using TextRank algorithm from the graph and make feature which are pairs of words which are each important word and a word adjacent to the important word. We use classifiers: SVM, Na$\ddot{i}$ve Bayesian classifier, Maximum Entropy Model, and k-NN classifier. We use non-cross-posted version of 20 Newsgroups data set. In consequence, we had an improved performance in whole classifiers, and the result tells that is a possibility of TextRank algorithm in text categorization.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.84-90
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• 2010

DBMS is used for a range of applications from data warehousing through on-line transaction processing. As a result of this demand, DBMS has continued to grow in terms of its size. This growth invokes the most important issue of manually tuning the performance of DBMS. The DBMS tuning should be adaptive to the type of the workload put upon it. But, identifying workloads in mixed database applications might be quite difficult. Therefore, a method is necessary for identifying workloads in the mixed database environment. In this paper, we propose a SVM workload classifier to automatically identify a DBMS workload. Database workloads are collected in TPC-C and TPC-W benchmark while changing the resource parameters. Parameters for SVM workload classifier, C and kernel parameter, were chosen experimentally. The experiments revealed that the accuracy of the proposed SVM workload classifier is about 9% higher than that of Decision tree, Naive Bayes, Multilayer perceptron and K-NN classifier.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.498-504
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• 2004

This paper presents a new text classifier based on a feature projection technique. In feature projections, training documents are represented as the projections on each feature. A classification process is based on individual feature projections. The final classification is determined by the sum from the individual classification of each feature. In our experiments, the proposed classifier showed high performance. Especially, it have fast execution speed and robustness with noisy data in comparison with k-NN and SVM, which are among the state-of-art text classifiers. Since the algorithm of the proposed classifier is very simple, its implementation and training process can be done very simply. Therefore, it can be a useful classifier in text classification tasks which need fast execution speed, robustness, and high performance.

• Smart Media Journal
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• v.3 no.2
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• pp.14-19
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• 2014

This paper presents a novel approach to recognize human gestures using k-NN and DTW based on Con tactless Electronic Potential Sensor(CEPS) in the smart devices such as smart TV and smart-phone in the proposed method, we used a Kalman filter to remove noise on gesture signal from CEPS and a PCA algorithm is utilized for reducing the dimensionality of gesture signal without data losses. And then in order to categorize gesture signals, k-NN classifier with DTW distance measure is considered. In the experimental result, we evaluate recognition performance with CEPS gesutres signal form the above two types of smart devices, and we can successfully identify five different gestures with more than 90% of recognition accuracy.

• Proceedings of the Korean Society for Information Management Conference
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• 2004.08a
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• pp.163-166
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• 2004

텍스트 범주화에 관한 선행연구에서 자주 사용되면서 좋은 성능을 보인 자질 선정 기법은 문헌빈도와 카이제곱 통계량 등이다. 그러나 이들은 단어 자체가 갖고 있는 모호성은 제거하지 못한다는 단점이 있다. 본 연구에서는 kNN 분류기를 이용한 범주화 실험에서 단어간의 상호 관련성이 자동적으로 유도됨으로써 단어 자체 보다는 단어의 개념을 분석하는 잠재의미색인 기법을 자질 선정 방법으로 제안한다.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.77-85
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• 2011

It is believed that the anomalies or diseases of human organs are identified by the analysis of the patterns. This paper proposes a new classification technique for the identification of cancer disease using the proteome patterns obtained from two-dimensional polyacrylamide gel electrophoresis(2-D PAGE). In the new classification method, three different classification methods such as support vector machine(SVM), multi-layer perceptron(MLP) and k-nearest neighbor(k-NN) are extended by multi-boosting method in an array of subclassifiers and the results of each subclassifier are merged by ensemble method. Genetic algorithm was applied to obtain optimal feature set in each subclassifier. We applied our method to empirical data set from cancer research and the method showed the better accuracy and more stable performance than single classifier.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1034-1038
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• 2005

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.