• Journal of Information Management
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• v.25 no.3
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• pp.1-27
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• 1994

This study intends to discuss the theories and case studies related to the selection and evaluation of information resources. The studies for the selection and evaluation of information resources can be divided into as follows : statistical methods, cost-effectiveness methods and expert system methods. Auther tried to discuss problems and prospects of the theoretical backgrounds and applications of expert system for the selection and evaluation of information resources.

• Journal of Information Management
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• v.25 no.2
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• pp.1-30
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• 1994

This study intends to discuss the theories and case studies related to the selection and evaluation of information resources. The studies for the selection and evaluation of information resources can be divided into as follows : statistical methods, cost-effectiveness methods and expert system methods. I also tried to discuss problems and prospects of the theoretical backgrounds and applications of expert system for the selection and evaluation of information resources.

• Journal of Information Technology Services
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• v.2 no.1
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• pp.123-133
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• 2003

Because of rapid evolution of software technique, numerous software professionals have been concerned with component based development methodologies. However, it is hard to find out a systematic technique for the selection of COTS (Commercial Off The Shelf) component in consumer position. Up to date, the major of component quality evaluation is object-oriented metric based evaluation methodology. But this paper present four step process and evaluation criteria based on MCDM (Multiple Criteria Decision Making) technique for optimal COTS component selection in consumer position. Weconsidered funtionality, efficiency, usability based on ISO/IEC 9126 for quality measurement and executed practical analysis about commercial EJB component in internet. This paper show that the proposed selection technique is applicable to optimal COTS component selection.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.91-97
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• 2019

In automatic map generalization, the formalization of cartographic principles is important. This study proposes and evaluates the selection method for road network generalization that analyzes existing maps using reverse engineering and formalizes the selection rules for the road network. Existing maps with a 1:5,000 scale and a 1:25,000 scale are compared, and the criteria for selection of the road network data and the relative importance of each network object are determined and analyzed using $T{\ddot{o}}pfer^{\prime}s$ Radical Law as well as the logistic regression model. The selection model derived from the analysis result is applied to the test data, and road network data for the 1:25,000 scale map are generated from the digital topographic map on a 1:5,000 scale. The selected road network is compared with the existing road network data on the 1:25,000 scale for a qualitative and quantitative evaluation. The result indicates that more than 80% of road objects are matched to existing data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.171-177
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• 2019

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.377-381
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• 2010

In 2007 the Nuclear Energy Agency's Committee on the Safety of Nuclear Installations published Best Practice Guidelines for the use of CFD in Nuclear Reactor Safety. This paper provides an overview of the document' contents and highlights a few of its recommendations. The document covers the full extent of a CFD analysis from initial problem definition and selection of an appropriate tool for the analysis, through final documentation of results. It provides advice on selection of appropriate simulation software, mesh construction, and selection of physical models. In addition it contains extensive discussion of the verification and validation process that should accompany any high-quality CFD analysis.

• Journal of Information Management
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• v.36 no.4
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• pp.51-69
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• 2005

High-speed classification method becomes an important research issue in text categorization systems. A fast text categorization technique, named feature value voting, is introduced recently on the text categorization problems. But the classification accuracy of this technique is not good as its classification speed. We present a novel approach for feature selection, named document-side feature selection, and apply it to feature value voting method. In this approach, there is no feature selection process in learning phase; but realtime feature selection is executed in classification phase. Our results show that feature value voting with document-side feature selection can allow fast and accurate text classification system, which seems to be competitive in classification performance with Support Vector Machines, the state-of-the-art text categorization algorithms.

• ETRI Journal
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• v.33 no.6
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• pp.904-913
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• 2011

Three-dimensional (3D) memories using through-silicon vias (TSVs) as vertical buses across memory layers will likely be the first commercial application of 3D integrated circuit technology. The memory dies to stack together in a 3D memory are selected by a die-selection method. The conventional die-selection methods do not result in a high-enough yields of 3D memories because 3D memories are typically composed of known-good-dies (KGDs), which are repaired using self-contained redundancies. In 3D memory, redundancy sharing between neighboring vertical memory dies using TSVs is an effective strategy for yield enhancement. With the redundancy sharing strategy, a known-bad-die (KBD) possibly becomes a KGD after bonding. In this paper, we propose a novel die-selection method using KBDs as well as KGDs for yield enhancement in 3D memory. The proposed die-selection method uses three search-space conditions, which can reduce the search space for selecting memory dies to manufacture 3D memories. Simulation results show that the proposed die-selection method can significantly improve the yield of 3D memories in various fault distributions.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1564-1569
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• 2009

Project managers play a key role in cost, time, and quality of a project. Selection of an appropriate project manager, therefore, is considered as one of the most important decisions in any construction project. It should be noted that most important decision makings are carried out by the project manager throughout the project. Traditionally, project manager selection in construction companies in Iran is through organizing an interview with candidates and selecting the most appropriate choice in accordance with the capabilities, potentials and individual specifications coupled with the requirements of the project. In the same direction, organizing interview on selection of appropriate candidate is usually carried out by senior managers of companies. Determination of the most important criteria for selection of project managers and also identification of significance coefficient of each criterion can highly help senior managers of companies to make sound selection decisions. In this paper, a numerical model has been considered for determination of significance of each criterion, details of which are submitted for selection of project manager in Iranian petrochemical, oil and gas sector companies. For this reason, all criteria- considered by senior managers of the companies under study- are first determined. Then, information obtained through 38 interviews, conducted by senior managers of the mentioned companies while selecting project manager, is analyzed. Significant coefficient of each criterion is calculated through the accumulated data using fuzzy curves method.