Two-stage extreme learning machine for high-dimensional data

Liu, Peng; Huang, Yihua; Meng, Lei; Gong, Siyuan; Zhang, Guopeng

doi:10.1007/s13042-014-0292-7

Cited by 24 publications

(8 citation statements)

References 19 publications

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“…Kumar and Minz (2014) pointed out a number of related works which have focused on comparing several feature selection methods for different domain problems. Others have proposed novel feature selection algorithms featuring filter (Lim, Lee, & Kim, 2017; Wang, Wei, Yang, & Wang, 2017), wrapper (Das, Das, & Ghosh, 2017; Zhang et al, 2016), and embedded (Liu, Huang, Meng, Gong, & Zhang, 2016; Zhu, Zhu, Hu, Zhang, & Zuo, 2017) techniques. However, many of these novel algorithms have been developed based only on one type of selection technique, filter, wrapper, or embedded feature selection processes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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Feature selection is a process aimed at filtering out unrepresentative features from a given dataset, usually allowing the later data mining and analysis steps to produce better results. However, different feature selection algorithms use different criteria to select representative features, making it difficult to find the best algorithm for different domain datasets. The limitations of single feature selection methods can be overcome by the application of ensemble methods, combining multiple feature selection results. In the literature, feature selection algorithms are classified as filter, wrapper, or embedded techniques. However, to the best of our knowledge, there has been no study focusing on combining these three types of techniques to produce ensemble feature selection. Therefore, the aim here is to answer the question as to which combination of different types of feature selection algorithms offers the best performance for different types of medical data including categorical, numerical, and mixed data types. The experimental results show that a combination of filter (i.e., principal component analysis) and wrapper (i.e., genetic algorithms) techniques by the union method is a better choice, providing relatively high classification accuracy and a reasonably good feature reduction rate.

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Section: Introductionmentioning

confidence: 99%

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

et al. 2020

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“…ELM is now modified for big data. Liu and Huang [98] used ELM to reduce the dimension of high-dimensional data by spectral regression. Then, the output weight can be obtained.…”

Section: Elm For Big Datamentioning

confidence: 99%

“…The proposed algorithm is simple and effective for binary classification. Liu and Huang [98] [198] Elastic ELM was proposed for big data learning.…”

Section: Elm Vs Support Vector Machine (Svm)mentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

176

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…Since the classification ability is quantified by the generalizaiton error, we will attempt to develop a convergence bound of the generalization error of HSIC-FMKL based on the established theory of Rademacher complexities. Besides, expanding the proposed model to multiple kernel clustering [69], extreme learning machine [70] and domain transfer learning [71], as well as adding more complicated complicated kernels such as the Chebyshev kernel and Hermite kernel [72] into the pool of base kernels for MKL are also important issues to be investigated.…”

Section: Conclusion and Further Studymentioning

confidence: 99%

Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert–Schmidt Independence Criterion

Wang

Zhang

2018

IEEE Trans. Fuzzy Syst.

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Multiple kernel learning (MKL) is a principled approach to kernel combination and selection for a variety of learning tasks such as classification, clustering and dimensionality reduction. In this paper, we develop a novel fuzzy multiple kernel learning model based on the Hilbert-Schmidt independence criterion (HSIC) for classification, which we call HSIC-FMKL. In this model, we first propose an HSIC Lasso-based MKL formulation, which not only has a clear statistical interpretation that minimum redundant kernels with maximum dependence on output labels are found and combined, but also enables the global optimal solution to be computed efficiently by solving a Lasso optimization problem. Since the traditional support vector machine (SVM) is sensitive to outliers or noises in the data set, fuzzy support vector machine (FSVM) is used to select the prediction hypothesis once the optimal kernel has been obtained. The main advantage of FSVM is that we can associate a fuzzy membership with each data point such that these data points can have different effects on the training of the learning machine. We propose a new fuzzy membership function using a heuristic strategy based on the HSIC. The proposed HSIC-FMKL is a two-stage kernel learning approach and the HSIC is applied in both stages. We perform extensive experiments on real-world data sets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed model in terms of prediction accuracy.

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Two-stage extreme learning machine for high-dimensional data

Cited by 24 publications

References 19 publications

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results

A review on extreme learning machine

Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert–Schmidt Independence Criterion

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