Network Intrusion Detection Model With Clustering Ensemble Method

Chen, Liang-Wei

doi:10.14257/ijsia.2015.9.11.23

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…In literature, several approaches for classifiers combination proposed. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], [22,23,24,25,26,27,28,29,30,31]…”

Section: Hybrid and Ensemble Pattern Recognitionmentioning

confidence: 99%

Review of IDS Develepment Methods in Machine Learning

Aburomman

Reaz

2016

IJECE

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<p>Due to the rapid advancement of knowledge and technologies, the problem of decision making is getting more sophisticated to address, therefore the inventing of new methods to solve it is very important. One of the promising directions in machine learning and data mining is classifier combination. The popularity of this approach is confirmed by the still growing number of publications. This review paper focuses mainly on classifier combination known also as combined classifier, multiple classifier systems, or classifier ensemble. Eventually, recommendations and suggestions have also included.</p>

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“…In literature, several approaches for classifiers combination proposed. [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], [22,23,24,25,26,27,28,29,30,31]…”

Section: Hybrid and Ensemble Pattern Recognitionmentioning

confidence: 99%

Review of IDS Develepment Methods in Machine Learning

Aburomman

Reaz

2016

IJECE

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“…Therefore, it can overcome the problem of the FCM that the FCM is integrated with kernel method. Chen [14] employed fuzzy kernel c-means as basic clustering for network intrusion detection. Senthil and Chandrakumar [15] introduced one kind of kernel fuzzy c-means based on Gaussian function for the purpose of segmentation of medical images.…”

Section: Introductionmentioning

confidence: 99%

Business Failure Prediction using Fuzzy C-Means with Kernel Method

Wang¹,

Wu²,

Wu³

2017

dtssehs

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Abstract:To propose the new prediction method of Kernel Fuzzy C-Means (KFCM) for business failure. Fuzzy C-Means (FCM) algorithm fails to deal with non-spherical clusters and incomplete data, while the kernel method can map the low-dimensional data into highdimensional feature space which is easier to be separated. Therefore, kernel method is integrated with the FCM to solve the problems of FCM. To fully reflect the performance of different kernel functions, KFCM respectively adopts three kernel functions which include Gaussian, Polynomial, Sigmoid kernel. The paper employs the financial data from Chinese quoted companies to predict the business failure. The prediction outcomes of three KFCMs are not only made a comparison to each other, but also compared with the standard FCM. It can show that KFCMs have better classification accuracy than FCM, but each of them has its advantages for normal and failing companies.

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Network Intrusion Detection Model With Clustering Ensemble Method

Abstract: Abstract

Cited by 2 publications

References 17 publications

Review of IDS Develepment Methods in Machine Learning

Review of IDS Develepment Methods in Machine Learning

Business Failure Prediction using Fuzzy C-Means with Kernel Method

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