• Journal of the Korea Society of Computer and Information
• /
• v.26 no.7
• /
• pp.1-7
• /
• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.2 s.191
• /
• pp.118-126
• /
• 2007

Most of materials receive forces in use so that the characteristics of materials must be considered in system design to prevent deformation or destruction. Mechanical properties of materials can be expressed as responsible level of material itself under the exterior operation. Main mechanical properties are strength, hardness, ductility and stiffness. Currently, among major measure facilities to measure the mechanical properties, advanced indentation technique has important use in industrial areas due to nondestructive and easy applications for mechanical tensile properties and evaluation of residual stress of materials. This study is to find the optimum experimental condition about residual stress advanced indentation technique for accurate analysis of the welded joint of steel materials through indentation load-depth curve obtained from cruciform specimen experiment. Optimum selection was applied to the welded joint of real steel materials to find out non-equi-biaxial stress state and the results were compared with general residual stress analyzing method fur verification.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1995.10a
• /
• pp.197-206
• /
• 1995

In structural design procedure, The procedure of member selection manages complex data relationship and reflects structural expert's knowledge. It is a difficult problem to construct an effective system with the conventional l programming technique. Knowledge_based s!'stem is a software system capable of supporting the explicit representation of expert's knowledge in member selection process through member data and reasoning mechanisms. This study describes useful methodology for structuring knowledge and representing relation between member data and knowledge. And this study shows the application of this member for member selection in the steel structural design.

• Interdisciplinary Bio Central
• /
• v.4 no.4
• /
• pp.14.1-14.6
• /
• 2012

Splice site prediction in DNA sequence is a basic search problem for finding exon/intron and intron/exon boundaries. Removing introns and then joining the exons together forms the mRNA sequence. These sequences are the input of the translation process. It is a necessary step in the central dogma of molecular biology. The main task of splice site prediction is to find out the exact GT and AG ended sequences. Then it identifies the true and false GT and AG ended sequences among those candidate sequences. In this paper, we survey research works on splice site prediction based on support vector machine (SVM). The basic difference between these research works is nucleotide encoding technique and SVM kernel selection. Some methods encode the DNA sequence in a sparse way whereas others encode in a probabilistic manner. The encoded sequences serve as input of SVM. The task of SVM is to classify them using its learning model. The accuracy of classification largely depends on the proper kernel selection for sequence data as well as a selection of kernel parameter. We observe each encoding technique and classify them according to their similarity. Then we discuss about kernel and their parameter selection. Our survey paper provides a basic understanding of encoding approaches and proper kernel selection of SVM for splice site prediction.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.279-282
• /
• 2001

This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) Model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

• Proceedings of the KSR Conference
• /
• 2008.06a
• /
• pp.614-622
• /
• 2008

It has been studying actively that the process and the methodology for the safety security of the system suggested from international standards and domestic/abroad materials in Korea. However, these process & methodology can generate a lot of errors and deficiencies because the system is applied without considering the system characteristics and its scope (such as hardware, software, interface, etc.). Therefore, the type is defined as the safety process with the basis on general development process in this study, the potential hazards of the mock-up system which is developed are extracted thorough selecting a technique according to each type. In addition to it, the effect is compared and analyzed with various technique selection by each phase of the development process.

• The KIPS Transactions:PartB
• /
• v.14B no.2
• /
• pp.135-140
• /
• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.12
• /
• pp.5865-5872
• /
• 2011

Multi-hop communication in clustering system is the technique that forms the cluster to aggregate the sensing data and transmit them to base station through midway cluster head. Cluster head around base station send more packet than that of far from base station. Because of this hot spot problem occurs and cluster head around base station increases energy consumption. In this paper, I propose a cluster group head selection using trajectory clustering technique(CHST). CHST select cluster head and group head using trajectory clustering technique and fitness function and it increases the energy efficiency. Hot spot problem can be solved by selection of cluster group with multi layer and balanced energy consumption using it's fitness function. I also show that proposed CHST is better than previous clustering method at the point of network energy efficiency.

• Convergence Security Journal
• /
• v.19 no.4
• /
• pp.87-96
• /
• 2019

Recently, with the rapid development of video equipment and technology, tremendous video information is produced and utilized in military domain to acquire battlefield information or for effective command control. Note that the video playback devices currently used in the military domain ranges from low-performance tactical multi-functional terminals (TMFT) to high-performance video servers and the networks where the video information is transmitted also range from the low speed tactical information and communication network (TICN) to ultra-high speed defense broadband converged networks such as M-BcN. Therefore, there is a need for an efficient streaming technique that can efficiently transmit defense video information in heterogeneous communication equipment and network environments. To solve the problem, this paper proposes a Scalable Video Coding (SVC) and balanced selection algorithm based Peer-to-Peer (P2P) streaming technique and the feasibility of the proposed technique is verified by simulations. The simulation results based on our BitTorrent simulator show that the proposed balanced selection scheme outperforms the sequential or rarest selection algorithm.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.1
• /
• pp.21-28
• /
• 2015

Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.