Multiple kernel learning with hybrid kernel alignment maximization

Wang, Yueqing; Liu, Xinwang; Dou, Yong; Lv, Qi; Lu, Yao

doi:10.1016/j.patcog.2017.05.005

Cited by 29 publications

(14 citation statements)

References 5 publications

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“…This optimization can be considered as a different mathematical model for obtaining the kernel parameters and their combination. Wang et al [28] noted that there are two types of multiple kernel learning methods including one-stage and two-stage methods. Actually, they correspond to the multiple learning methods dependent and independent of the classifier, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection

2020

KSII TIIS

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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.

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Section: Related Workmentioning

confidence: 99%

A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection

2020

KSII TIIS

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“…There is no perfect theoretical basis for the construction or selection of kernel function. In order to solve these problems, a lot of multiple kernel learning (MKL) methods using kernel combinations were proposed [5][6][7]. Gönen et al gave a taxonomy of multiple kernel learning algorithms and reviewed them in detail [8].…”

Section: Introductionmentioning

confidence: 99%

“…, e is the base of the natural logarithm, and m is the number of elements of the base kernel function set K. The generalization bound can be summarized from inequations (3) to (5). The local Lipschitz constant C φ λ , M φ λ is estimated according to Equations (6) and (7), where φ is the loss function, and λ is the regularization parameter of a two-layer minimization problem:…”

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confidence: 99%

Self-Adaptive Deep Multiple Kernel Learning Based on Rademacher Complexity

et al. 2019

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The deep multiple kernel learning (DMKL) method has caused widespread concern due to its better results compared with shallow multiple kernel learning. However, existing DMKL methods, which have a fixed number of layers and fixed type of kernels, have poor ability to adapt to different data sets and are difficult to find suitable model parameters to improve the test accuracy. In this paper, we propose a self-adaptive deep multiple kernel learning (SA-DMKL) method. Our SA-DMKL method can adapt the model through optimizing the model parameters of each kernel function with a grid search method and change the numbers and types of kernel function in each layer according to the generalization bound that is evaluated with Rademacher chaos complexity. Experiments on the three datasets of University of California—Irvine (UCI) and image dataset Caltech 256 validate the effectiveness of the proposed method on three aspects.

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“…It requires prior knowledge of the data distribution in the feature space. However, when the data distribution is unknown or the distribution is complex, multiple kernel (MK) functions are combined to construct the feature space and strengthen the learning ability of the model [28][29][30][31]. Therefore, the problem is reduced to a search for approximate parameters with respect to a given dataset.…”

Section: Introductionmentioning

confidence: 99%

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

2019

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In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.

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Multiple kernel learning with hybrid kernel alignment maximization

Cited by 29 publications

References 5 publications

A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection

A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection

Self-Adaptive Deep Multiple Kernel Learning Based on Rademacher Complexity

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

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