• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.509-515
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• 2017

In this paper, for a cooperative communication system with multiple relays, we consider a relay selection method by exploiting the trust degree information of relay nodes. In the cooperative communication system, we interpret the trust degree of relays as the probability that relay helps the communication between the transmitter and receiver. We first provide an expected achievable rate at the receiver by taking into account the both cases that the relay helps the transmission of transmitter and the relay does not help the transmission of transmitter according to its trust degree. For given trust degree information, we propose an efficient relay selection method to maximize the expected achievable rate at the receiver. For the various configurations, the simulation results confirm that the proposed relay selection method outperforms the conventional relay selection method, which does not consider the trust degree of relay nodes.

• Journal of Korean Library and Information Science Society
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• v.43 no.3
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• pp.457-479
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• 2012

This paper strives to illustrate the selection processes in public libraries. It specifically attempts to survey the budget allocation, collection development policy, usage of selection criteria, and priority of selection decision in collection development units in public libraries. Staff structure, committee activities, methods of selection, usage of selection tools and librarians' recognitions about selection process are also investigated. Data are drawn from a survey with 315 public libraries in the country. Specific statistics to be analyzed via literature, although not detailed in nature, are scrutinized as well. As a conclusion, the paper discusses such an issue as current situation in selection of materials public libraries and possible impetus toward a better collection development process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Korean Journal of Geomatics
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• v.1 no.1
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• pp.95-101
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• 2001

In a route plan, the route selection is a complicated problem to consider the spatial distribution and influence through overall related data and objective analysis on the social, economic and technical condition. The developed system in this study was compared and estimated by deciding a practical section for its validity and efficiency. Using Geographic Information System (GIS), the various information required for route selections in database was constructed, the characteristics of subject area by executing three-dimensional terrain analysis was grasped effectively, and the control point through buffering, overlay and location operation was extracted. An optimum route was selected by calculating the sum of alternatives to the sub-criteria weight, and from this result, there is a difference between real route and proposed route according to the prioritization of decision criteria based on the importance. This research could be constructed and applied geospatial information to the reasonable route plan and an optimum route selection efficiently using GIS. Therefore, the applications are presented by applying Analytic Hierarchy Process (AHP) to the decision-making of information needed in route selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3008-3028
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• 2022

In this paper, we investigate traffic scheduling for a delay-sensitive multi-hop relay network, and aim to minimize the priority-based end-to-end delay of different data packet via joint relay selection, subcarrier assignment, and power allocation. We first derive the priority-based end-to-end delay based on queueing theory, and then propose a two-step method to decompose the original optimization problem into two sub-problems. For the joint subcarrier assignment and power control problem, we utilize an efficient particle swarm optimization method to solve it. For the relay selection problem, we prove its convexity and use the standard Lagrange method to deal with it. The joint relay selection, subcarriers assignment and transmission power allocation problem for each hop can also be solved by an exhaustive search over a finite set defined by the relay sensor set and available subcarrier set. Simulation results show that both the proposed routing scheme and the resource allocation scheme can reduce the average end-to-end delay.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1123-1134
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• 2007

This article addresses the problem of testing whether the shape parameters in k independent Weibull populations are equal. We propose a Bayesian model selection procedure for equality of the shape parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedure based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real example are provided.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1167-1176
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• 2005

Tweedie (1957a) proposed a method for the analysis of residuals from an inverse Gaussian population paralleling the analysis of variance in normal theory. He called it the analysis of reciprocals. In this paper, we propose a Bayesian model selection procedure based on the fractional Bayes factor for the analysis of reciprocals. Using the proposed model selection procedures, we compare with the classical tests.

• Journal of information and communication convergence engineering
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• v.3 no.4
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1297-1304
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• 2008

This paper proposes a machine learning approach for language-independent sentence boundary detection. The proposed method requires no heuristic rules and language-specific features, such as part-of-speech information, a list of abbreviations or proper names. With only the language-independent features, we perform experiments on not only an inflectional language but also an agglutinative language, having fairly different characteristics (in this paper, English and Korean, respectively). In addition, we obtain good performances in both languages. We have also experimented with the methods under a wide range of experimental conditions, especially for the selection of useful features.

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