• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.9
• /
• pp.1513-1527
• /
• 2011

This paper investigates the capacity rate problems for a joint decode-and-forward (JDF) based two-way relaying with network coding. We first characterize the achievable rate region for a conventional three-node network scenario along with the calculation of the corresponding maximal sum-rate. Then, for the goal of maximizing the system sum-rate, opportunistic relay selection is examined for multi-relay networks. As a result, a novel strategy for the implementation of relay selection is proposed, which depends on the instantaneous channel state and allows a single best relay to help the two-way information exchange. The JDF scheme and the scheme using relay selection are analyzed in terms of outage probability, after which the corresponding exact expressions are developed over Rayleigh fading channels. For the purpose of comparison, outage probabilities of the amplify-and-forward (AF) scheme and those of the scheme using relay selection are also derived. Finally, simulation experiments are done and performance comparisons are conducted. The results verify that the proposed strategy is an appropriate method for the implementation of relay selection and can achieve significant performance gains in terms of outage probability regardless of the symmetry or asymmetry of the channels. Compared with the AF scheme and the scheme using relay selection, the conventional JDF scheme and that using relay selection perform well at low signal-to-noise ratios (SNRs).

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.63-71
• /
• 2016

This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.6
• /
• pp.1191-1197
• /
• 2009

Most Bayesian approaches to model selection of wavelet analysis have drawbacks that computational cost is expensive to obtain accuracy for the fitted unknown function. To overcome the drawback, this article introduces loss functions which are criteria for level dependent threshold selection in wavelet based Bayesian methods with arbitrary size and regular design points. We demonstrate the utility of these criteria by four test functions and real data.

• Journal of Information Processing Systems
• /
• v.2 no.2
• /
• pp.130-134
• /
• 2006

In the distributed replicating server model, the provision of replicated services will improve the performance of the providing service and efficiency for clients. Efficiently composing the server selection algorithm decreases the retrieval time for replicated data. In this paper, we define the system model that selects and connects the replicated server that provides an optimal service using the server-side downstream measurement and propose a server selection algorithm.

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

• Journal of Communications and Networks
• /
• v.10 no.2
• /
• pp.175-185
• /
• 2008

In this paper, we investigate the effect of cooperative and selection relaying schemes on multiuser diversity in downlink cellular systems with fixed relay stations (RSs). Each mobile station (MS) is either directly connected to a base station (BS) and/or connected to a relay station. We first derive closed-form solutions or upper-bound of the ergodic and outage capacities of four different downlink data relaying schemes: A direct scheme, a relay scheme, a selection scheme, and a cooperative scheme. The selection scheme selects the best access link between the BS and an MS. For all schemes, the capacity of the BS-RS link is assumed to be always larger than that of RS-MS link. Half-duplex channel use and repetition based relaying schemes are assumed for relaying operations. We also analyze the system capacity in a multiuser diversity environment in which a maximum signal-to-noise ratio (SNR) scheduler is used at a base station. The result shows that the selection scheme outperforms the other three schemes in terms of link ergodic capacity, link outage capacity, and system ergodic capacity. Furthermore, our results show that cooperative and selection diversity techniques limit the performance gain that could have been achieved by the multiuser diversity technique.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3725-3748
• /
• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.240-259
• /
• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.2
• /
• pp.191-196
• /
• 2009

In this paper, we proposed a feature selection method using Binary Particle Swarm Optimization(BPSO) and Mutual information. This proposed method consists of the feature selection part for selecting candidate feature subset by mutual information and the optimal feature selection part for choosing optimal feature subset by BPSO in the candidate feature subsets. In the candidate feature selection part, we computed the mutual information of all features, respectively and selected a candidate feature subset by the ranking of mutual information. In the optimal feature selection part, optimal feature subset can be found by BPSO in the candidate feature subset. In the BPSO process, we used multi-object function to optimize both accuracy of classifier and selected feature subset size. DNA expression dataset are used for estimating the performance of the proposed method. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

• The Korean Journal of Health Service Management
• /
• v.3 no.1
• /
• pp.47-58
• /
• 2009

This study is to grasp selection factors and habits of information searching of customers of ophthalmic service and to verify the differences in them and to investigate how they affect in selecting medical institute by demographic sociological characters, selection factors by classification and habits of information searching, how many times they used and the type of medical treatment. The result of analysis of importance of selection factors of medical institute, it showed that doctors' career were evaluated high by classification and it showed in order of university hospital, hospital, clinic in facilities and equipment and in order of university hospital, clinic, hospital in distance transportation Analysis of importance of selection factors by sex distinction, it showed that doctors' career were high for both male and female and according to the result of analysis of selection factors by an age, doctors' career variable was measured high and it showed in order of facilities, equipment, distance and convenient transportation. The result of analysis by the form of medical treatment, doctors' career were measured high in all diseases. Facilities and equipment were measured high in case of a corrective operation of eyesight and distance transportation variable showed high in simple eye diseases. According to the result of analysis of habits of searching information by utility frequency, one's own experience in the past(direct visits) was the highest over all and it showed in order of introduction of other ophthalmic department in case of people who go to the institutes many times.