• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.828-833
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• 2005

Kernel methods have shown to improve the performance of conventional linear classification algorithms for complex distributed data sets, as mapping the data in input space into a higher dimensional feature space(7). In this paper, we propose a fuzzy kernel K-nearest neighbor(fuzzy kernel K-NN) algorithm, which applies the distance measure in feature space based on kernel functions to the fuzzy K-nearest neighbor(fuzzy K-NN) algorithm. In doing so, the proposed algorithm can enhance the Performance of the conventional algorithm, by choosing an appropriate kernel function. Results on several data sets and segmentation results for real images are given to show the validity of our proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.817-821
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• 2001

In this paper, a new learning methodology for kernel methods that results in a sparse representation of kernel space from the training patterns for classification problems is suggested. Among the traditional algorithms of linear discriminant function, this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epoches. For sequential learning of kernel methods, extended SVM and kernel discriminant function are defined. Systematic derivation of learning algorithm is introduced. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.173-180
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• 2007

This paper designs the kernel hardening function by recovering the kernel stack fame to reduce the system error or panic due to the kernel code error. The suggested kernel hardening function guarantees normal system operation by recovering the incorrect address of the kernel stack kernel. The suggesting kernel hardening mechanism is applied to the network module of Linux which is much using put. I experimented the kernel hardening function at the network module of the Linux by forcing panic code.

• Bulletin of the Korean Mathematical Society
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• v.41 no.3
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• pp.403-412
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• 2004

We obtain a kernel quantile process based on the kernel quantile estimator and prove the uniform consistency of the kernel quantile process by developing that of the usual sample quantile process. We apply our result to the classical kernel type processes.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.1
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• pp.59-65
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• 1993

In a spark ignition engine, in order to make research on flame propagation, attentive concentration should be paid on initial combustion stage about the formation and development of flame. In addition, the initial stage of combustion governs overall combustion period in a spark ignition engine. With the increase of the size of flame kernel, it could reach initial flame stage easily, and the mixture could proceed to the combustion of stabilized state. Therefore, we must study the theoretical calculation of minimum flame kernel radius which effects on the formation and development of kernel. To calculate the minimum flame kernel radius, we must know the thermal conductivity, flame temperature, laminar burning velocity and etc. The thermal conductivity is derived from the molecular kinetic theory, the flame temperature from the chemical reaction equations and the laminar burning velocity from the D.K.Kuehl's formula. In order to estimate the correctness of the theoretically calculated minimum flame kernel radius, the researcheres compared it with the RMaly's experimental values.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Food Science and Biotechnology
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• v.16 no.6
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• pp.918-923
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• 2007

To investigate the relationship of wheat single kernel characteristics with end-use properties, 183 soft wheat cultivars and lines were evaluated for milling quality characteristics (kernel hardness, kernel and flour protein, flour ash), and end-use properties (i.e., as ingredients in sugar-snap cookies, sponge cake). Significant positive correlations occurred among wheat hardness parameters including near-infrared reflectance (NIR) score and single kernel characterization system (SKCS). The SKCS characteristics were also significantly correlated with conventional wheat quality parameters such as kernel size, wheat protein content, and straight-grade flour yield. The cookie diameter and cake volume were negatively correlated with NIR and SKCS hardness, and there was an inverse relationship between flour protein contents and kernel weights or sizes. Sugar-snap cookie diameter was positively correlated with sponge cake volume.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.254-268
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• 2013

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.