• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1571-1589
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• 2016

Nodes in MANETs are battery powered which makes energy an invaluable resource. In OLSR, MPRs are special nodes that are selected by other nodes to relay their data/control traffic which may lead to high energy consumption of MPR nodes. Therefore, employing energy efficient MPR selection mechanism is imperative to ensure prolonged network lifetime. However, misbehaving MPR nodes tend to preserve their energy by dropping packets of other nodes instead of forwarding them. This leads to huge energy loss and performance degradation of existing energy efficient MPR selection schemes. This paper proposes an energy efficient secure MPR selection (ES-MPR) technique that takes into account both energy and security metrics for MPR selection. It introduces the concept of 'Composite Eligibility Index' (CEI) to examine the eligibility of a node for being selected as an MPR. CEI is used in conjunction with willingness to provide distinct selection parameters for Flooding and Routing MPRs. Simulation studies reveal the efficiency of ES-MPR in selection of energy efficient secure and stable MPRs, in turn, prolonging the network operational lifetime.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.10-21
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• 2017

The purposes of this study were to verify the effects of HMR (Home Meal Replacement) selection attributes on satisfaction and loyalty for HMR. In addition, the moderating role of customer value was examined among selection attributes of HMR, satisfaction and loyalty for HMR. Using a convenience sampling method, the data were collected from those who have bought HMR in Seoul and Kyonggi area. After a total of 235 responses were collected, 220 were used for the analyses. The multiple regression analyses were conducted to test the hypotheses. The results are as follows. First, it was found that product practicality and cooking convenience of HMR selection attributes had an effect on satisfaction of HMR but that ingredients safety and package & circulation period did not have an effect on satisfaction of HMR. Second, satisfaction of HMR significantly impacted loyalty for HMR. Third, in low group for customer value, product practicality of HMR selection attributes had an positive effect on satisfaction of HMR, and ingredients safety of HMR selection attributes had an negative effect on satisfaction of HMR. In high group for customer value, cooking convenience of HMR selection attributes had an positive effect on satisfaction of HMR. In low group as high group for customer value, satisfaction of HMR had a greater impact on loyalty for HMR.

• Culinary science and hospitality research
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• v.23 no.7
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• pp.31-41
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• 2017

The purpose of this study was to verify the relationship of eating-out consumption propensity and selection attributes by consumers have been used dessert cafe and to provide the useful data for efficient establishing marketing strategies to dessert cafe managers. This survey was conducted from 7th to 22th on April, 2017 and a total of 250 responses were distributed, of which 232 were used for analysis, after excluding responses containing missing data. The results from this study are as follows. First, it was found that eating out enjoyment pursuit type and health pursuit type had significant effects on menu and service factor of selection attributes for dessert cafe. Second, only eating out enjoyment pursuit type had significant effects on visual factor of selection attributes for dessert cafe. Third, only health pursuit type had significant effects on health menu factor of selection attributes for dessert cafe. Fourth, economic value pursuit type and atmosphere pursuit type had significant effects on price factor of selection attributes for dessert cafe. This study contributes to useful results for establishing efficient marketing strategies to dessert cafe marketers by examining selection attribution of dessert cafe as recognizing eating-out consumption propensity.

• BMB Reports
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• v.51 no.1
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• pp.1-2
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• 2018

Positive selection on a new beneficial mutation generates a characteristic pattern of DNA sequence polymorphism when it reaches an intermediate allele frequency. On genome sequences of African Drosophila melanogaster, we detected such signatures of selection at 37 candidate loci and identified "sweeping haplotypes (SHs)" that are increasing or have increased rapidly in frequency due to hitchhiking. Based on geographic distribution of SH frequencies, we could infer whether selective sweeps occurred starting from de novo beneficial mutants under simple constant selective pressure. Single SHs were identified at more than half of loci. However, at many other loci, we observed multiple independent SHs, implying soft selective sweeps due to a high beneficial mutation rate or parallel evolution across space. Interestingly, SH frequencies were intermediate across multiple populations at about a quarter of the loci despite relatively low migration rates inferred between African populations. This invokes a certain form of frequency-dependent selection such as heterozygote advantage. At one locus, we observed a complex pattern of multiple independent that was compatible with recurrent frequency-dependent positive selection on new variants. In conclusion, genomic patterns of positive selection are very diverse, with equal contributions of hard and soft sweeps and a surprisingly large proportion of frequency-dependent selection in D. melanogaster populations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1961-1974
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• 2019

In this letter, a harmonic-mean-based dual-antenna selection scheme at relay node is proposed in two-way relaying networks (TWRNs). With well-designed distributed orthogonal concatenated Alamouti space-time block code (STBC), a dual-antenna selection problem based on the instantaneous achievable sum-rate criterion is formulated. We propose a low-complexity selection algorithm based on the harmonic-mean criterion with linearly complexity $O(N_R)$ rather than the directly exhaustive search with complexity $O(N^2_R)$. From the analysis of network outage performance, we show that the asymptotic diversity gain function of the proposed scheme achieves as $1/{\rho}{^{N_R-1}}$, which demonstrates one degree loss of diversity order compared with the full diversity. This slight performance gap is mainly caused by sacrificing some dual-antenna selection freedom to reduce the algorithm complexity. In addition, our proposed scheme can obtain an extra coding gain because of the combination of the well-designed orthogonal concatenated Alamouti STBC and the corresponding dual-antenna selection algorithm. Compared with the common-used selection algorithms in the state of the art, the proposed scheme can achieve the best performance, which is validated by numerical simulations.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.2 s.161
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• pp.206-216
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• 2007

The purpose of this study was to investigate the differences in the cosmetic purchasing behavior influenced by make-up preference images, and the orders of importance in the cosmetic store selection. The questionnaires were given to female residents in the ages between $20{\sim}45$ in Seoul and Kyung-gi province. 322 questionnaires were used for data analysis. The collected data were analyzed by using SPSS 10.0 software with various techniques such as Frequency analysis, Factor analysis, Cronbach's ${\alpha}$ reliability analysis, Paired t-test, ANOVA test and Duncan test. The results of this study were as follows: 1. After investigating how the make-up preference image influences the selection of the off-line cosmetic store, it was found out that the personal service, shopping convenience, and product composition had significant differences. 2. After investigating how the make-up preference image influences the selection of the on-line cosmetic store, it was found out that only the product composition had significant difference. 3. After studying the factors that influence the off-line cosmetic store selection, it was found that the personal service was considered most important. After studying the factors that influence the on-line cosmetic store selection, it was found that the price was considered most important.

• 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.