Joint hypergraph learning and sparse regression for feature selection

Zhang, Zhihong; Bai, Lu; Liang, Yuanheng; Hancock, Edwin R.

doi:10.1016/j.patcog.2016.06.009

Cited by 63 publications

(27 citation statements)

References 29 publications

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“…The twelve features are features of Modbus_traffic data set selected by FAFS method. (4,6,9,11,12,18,25,16,22,23,14) 0.5737 (4,6,9,11,12,18,25,22,23,14,16) 0.5737 (4,6,9,11,12,18,25,16,23,14,22) 0.5737 (1,4,6,9,11,12,16,22,23,14,25) 0.5737 … … … … (1,4,6,9,11,12,22,23,14,25,…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Feng¹,

Kang²,

Zhang³

et al. 2020

KSII TIIS

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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.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Over-fitting can be effectively avoided, and the complexity of time and space can be reduced by feature selection method adopted in initial data sets. The accuracy of detection can be improved as well [6].…”

Section: Introductionmentioning

confidence: 99%

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

Feng¹,

Kang²,

Zhang³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…(9) Update the luciferin l i and the radial range local-decision domain r i d . (10) if rand < r 1 do (11) N glowworms are divided into three subpopulations according to their MFD. (12) Perform the coevolution mechanism to create offspring glowworms and update their parent glowworms.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Different evaluation methods have great relationship with the optimal subset. For example, information theory [11][12][13], distance analysis [4], rough sets [14][15][16][17], and fractal dimension [18][19][20]. Fractal dimension is treated as an evaluation criterion, which attracts many scholars' attentions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Key Influencing Factors Selection Approach of P2P Lending Investment Risk

Xia

Zhu

et al. 2019

Mathematical Problems in Engineering

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Recent frequent “thunderstorm incidents” of P2P lending industry have caused the panic of industry investors. To predict the investment risk of P2P lending, we should scientifically and rationally analyze the key influencing factors of P2P lending investment risk. Existing key influencing factors selection methods mainly involve traditional statistical approaches and artificial intelligence methods. The traditional statistical approaches cannot deal with the high-dimensional nonlinear problems, and it cannot find the exact key influencing factors of the P2P lending investment risk. The artificial intelligence methods cannot recognize and learn the application background, and the selected attributes without active thinking and personal perception may not be the key influencing factors of P2P lending investment risk. To address the above issues, a novel key influencing factors selection approach of P2P lending investment risk is proposed by combining the proposed fireworks coevolution binary glowworm swarm optimization (FCBGSO), multifractal dimension (MFD), probit regression, and artificial prior knowledge. First, multifractal dimension combined with the proposed FCBGSO is used to select the preliminary influencing factors of the investment risk; second, the nonsignificant relevant attributes in the preliminary influencing factors are removed using the probit regression, and we add the influencing factors extracted from the original dataset of P2P lending using the artificial prior knowledge into the retaining influencing factors after removing one by one. A small and reasonable number of influencing factor subsets are achieved. Finally, we evaluate each influencing factors subset using extreme learning machine (ELM), and the subset with the best classification accuracy is efficiently achieved, i.e., it is the key influencing factors of P2P lending investment risk. Experimental results on the real P2P lending dataset from the Renrendai platform demonstrate that the proposed approach performs better than other state-of-the-art methods and that it has validity and effectiveness. It provides a new research idea for the key influencing factors selection of P2P lending investment risk.

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“…To reveal multiple properties of the data, hypergraph was introduced into the field of machine learning [35], [36], [37]. In hypergraph, each hyperedge contains more than two vertices while the traditional graph just has two vertices on each edge [38], [39]. Subsequently, a series of hypergraph learning methods have been developed for DR of the data.…”

Section: Introductionmentioning

confidence: 99%

Semisupervised Hypergraph Discriminant Learning for Dimensionality Reduction of Hyperspectral Image

Luo

Guo

Lin

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Semi-supervised learning is an effective technique to represent the intrinsic features of hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semi-supervised learning methods fail to consider multiple properties of HSI, which have restricted the discriminant performance of feature representation. In this paper, we introduce the hypergraph into semi-supervised leraning to reveal the complex multi-structures of HSI, and construct a semi-supervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The results indicate that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of HSI.

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Joint hypergraph learning and sparse regression for feature selection

Cited by 63 publications

References 29 publications

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

FAFS: A Fuzzy Association Feature Selection Method for Network Malicious Traffic Detection

A Novel Key Influencing Factors Selection Approach of P2P Lending Investment Risk

Semisupervised Hypergraph Discriminant Learning for Dimensionality Reduction of Hyperspectral Image

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