• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.87-94
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• 2004

The feature subset selection is used as a preprocessing step of a teaming algorithm. If collected data are irrelevant or redundant information, we can improve the performance of learning by removing these data before creating of the learning model. The feature subset selection can also reduce the search space and the storage requirement. This paper proposed a new feature subset selection algorithm that is using the heuristic function based on entropy to evaluate the performance of the abstracted feature subset and feature selection. The ACS algorithm was used as a search method. We could decrease a size of learning model and unnecessary calculating time by reducing the dimension of the feature that was used for learning.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.758-765
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• 2001

This paper proposes a method to automatically extract stable visual landmarks from sensory data. Given a 2D occupancy map, a mobile robot first extracts vertical line features which are distinct and on vertical planar surfaces, because they are expected to be observed reliably from various viewpoints. Since the feature information such as position and length includes uncertainty due to errors of vision and motion, the robot then reduces the uncertainty by matching the planar surface containing the features to the map. As a result, the robot obtains modeled stable visual landmarks from extracted features. This extraction process is performed on-line to adapt to an actual changes of lighting and scene depending on the robot’s view. Experimental results in various real scenes show the validity of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.148-151
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• 2010

분류 기법은 데이터 마이닝 기술 중 가장 잘 알려진 방법으로서, Decision tree, SVM(Support Vector Machine), ANN(Artificial Neural Network) 등 기법을 포함한다. 분류 기법은 이미 알려진 상호 배반적인 몇 개 그룹에 속하는 다변량 관측치로부터 각각의 그룹이 어떤 특징을 가지고 있는지 분류 모델을 만들고, 소속 그룹이 알려지지 않은 새로운 관측치가 어떤 그룹에 분류될 것인가를 결정하는 분석 방법이다. 분류기법을 수행할 때에 기본적으로 특징 공간이 잘 표현되어 있다고 가정한다. 그러나 실제 응용에서는 단일 특징으로 구성된 특징공간이 분명하지 않기 때문에 분류를 잘 수행하지 못하는 문제점이 있다. 본 논문에서는 이 문제에 대한 해결방안으로써 많은 정보를 포함하면서 빈발패턴에 대한 정보의 순실이 없는 닫힌 빈발패턴 기반 분류에 대한 연구를 진행하였다. 본 실험에서는 ${\chi}^2$(Chi-square)과 정보이득(Information Gain) 속성 선택 척도를 사용하여 의미있는 특징 선택을 수행하였다. 그 결과, 이 연구에서 제시한 척도를 사용하여 특징 선택을 수행한 경우, C4.5, SVM 과 같은 분류기법보다 더 향상된 분류 성능을 보였다.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.41-48
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• 2011

In feature selection, the selective evaluation scheme for Ant Colony Optimization(ACO) has recently been proposed, which reduces computational load by excluding unnecessary or less promising candidate solutions from the actual evaluation. Its superiority was supported by experimental results. However the experiment seems to be not statistically sufficient since it used only one dataset. The aim of this paper is to analyze convergence characteristics of the selective evaluation scheme and to make the conclusion more convincing. We chose three datasets related to handwriting, medical, and speech domains from UCI repository whose feature set size ranges from 256 to 617. For each of them, we executed 12 independent runs in order to obtain statistically stable data. Each run was given 72 hours to observe the long-time convergence. Based on analysis of experimental data, we describe a reason for the superiority and where the scheme can be applied.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.17-25
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• 2009

This paper suggests fuzzy-based Segment-Boost method and an effective method for face recognition using the fuzzy-based Segment-Boost. Fuzzy-based Segment-Boost eliminates the limitations of Segment-Boost, and it guarantees improved learning performance and the stability of the performance. By using the fuzzy theory, fuzzy-based Segment-Boost optimizes the selection number of sub-vectors, and leads the optimized learning performance. The fuzzy controller designed in this paper measures learning performance of the fuzzy-based Segment-Boost, and it controls the selection number of sub-vectors by inferring the optimized selection number. The simulation results show that the fuzzy controller inferred the selection number which is very approximate to the true optimized value. As a result, fuzzy-based Segment-Boost showed higher face recognition rate than compared boosting methods and it preserves the velocity of feature selection as fast as that of Segment-Boost. From the experimental results, it was proved that fuzzy-based Segment-Boost has improved and stable performances of learning, feature selection and face recognition.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.43-44
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• 2018

본 논문에서는 SDN환경에서 트래픽 분류를 위한 특징 선택 기법을 제안한다. 최근 들어 다양한 스마트폰 어플리케이션이나 다양한 IoT 기기들이 등장하고 있다. 다양한 IoT 기기나 어플리케이션은 엄청난 양의 트래픽을 발생시킨다. 엄청난 양의 트래픽은 전송속도를 저하시킬 뿐만 아니라, 높은 Quality of Service(QoS)를 보장하기가 힘들다. 또한 이러한 문제점들을 보안하기 위해 Software Defined Networking(SDN)이라는 기술이 빠른속도로 발전하고 있으며, 본 논문에서는 다양한 어플리케이션과 IoT 기기의 트래픽을 SDN환경에서 분류 하며, 트래픽의 분류 정확도와 더 빠른 분류를 위해 트래픽의 특징 선택 기법을 제안한다. 특징 선택을 수행한 뒤, 트래픽 분류를 진행하면 트래픽 분류 정확도를 높이고, 트래픽 분류시간은 줄어드는 효과를 보이며, 높은 QoS를 보장함으로써, 기존 네트워크 트래픽의 부하가 줄어드는 우수한 성능을 보인다.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.833-839
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• 2016

In this paper, we propose a selective feature extraction algorithm between Markov transition probability and co-occurrence probability for an effective image splicing detection. The Features used in our method are composed of the difference values between DCT coefficients in the adjacent blocks and the value of Kullback-Leibler divergence(KLD) is calculated to evaluate the differences between the distribution of original image features and spliced image features. KLD value is an efficient measure for selecting Markov feature or Co-occurrence feature because KLD shows non-similarity of the two distributions. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. To verify our algorithm we used grid search and 6-folds cross-validation. Based on the experimental results it shows that the proposed method has good detection performance with a limited number of features compared to conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.22-29
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• 2000

The purpose of this paper is to propose a method to maximize a content-based image retrieval efficiency in multiple images. To perform an image retrieval job efficiently, it is necessary to minimize the number of candidate-images. Furthermore, a miximum efficiency of image retrieval could not be expected if an image retrieval job in the multiple images is done on the basis of patterns of single image distinctive features. In this method, a multiple SOFM neural network system is adopted to select automatically distinctive feature patterns which have a maximum efficiency of image retrieval in the multiple images. In this method. an image retrieval efficiency is improved 3% than individual features and the number of candidate-images is reduced by the multiple SOFM neural network system.