IT Performance Key Factors Extraction Based on PCA Selective Ensemble

Ming Yan Hu; Wei Guo; Nan Zhao

doi:10.4028/www.scientific.net/AMM.121-126.4003

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126IT Performance Key Factors Extraction Based on PCA...

IT Performance Key Factors Extraction Based on PCA Selective Ensemble

Abstract:

The IT performance evaluation is a complex analysis system. Some key factors that can reflect the essential characteristics and be easy to acquire, should be extracted to help us to clarify evaluation, analysis and diagnoses. In this paper for the uncertainty of IT performance data, an algorithm of IT performance key factors extraction based on selective ensemble of principal component analysis (PCA) is proposed. Unlike the traditional PCA algorithm, PCA selective ensemble increase the difference degree between the main component and the others. Moreover it expands the selective ensemble in the application of unsupervised algorithm. The validity of the algorithm is verified through the analysis of the actual data.

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Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

4003-4007

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.4003

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Online since:

October 2011

Authors:

Ming Yan Hu, Wei Guo, Nan Zhao

Keywords:

It Performance, Key Factor, Principal Component Analysis (PCA), Selective Ensemble

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References

[1] Guo Wei, Zhao Nan and Shao Hongyu: International Conference on Challenges in Environmental Science and Computer Engineering, CESCE (2010), pp.21-24.

Google Scholar

[2] L. Breiman.: Machine Learning Vol. 24(1996), pp.123-140.

Google Scholar

[3] Schapire and Schapire, R.E.: Machine Learning Vol. 5(1990), p.197–227.

Google Scholar

[4] Z. H. Zhou, J. X. Wu and W. Tang: Artificial Intelligence Vol. 137(2002), pp.239-263.

Google Scholar

[5] Xu-Cheng Yin, Chang-Ping Liu and Zhi Han: Pattern Recognition Letters Vol. 26(2005), pp.2195-2205.

Google Scholar

[6] E. Bauer and R. Kohavi: Machine Learning Vol. 36(1999), pp.105-139.

Google Scholar

[7] Sridhar S. Kannurpatti and Bharat B. Biswal: Journal of Neuroscience Methods Vol. 146(2005), pp.61-68.

Google Scholar

[8] Zhou ZH and Tang W: Knowledge-Based Systems Vol. 19(2006), pp.78-91.

Google Scholar

[9] H. C. Kim, S. N. Pang, H. M. Je, et al.: Pattern Recognition Vol. 36(2003), pp.2757-2767.

Google Scholar

[10] Strehl A and Ghosh J.: The 18th National Conf. on Artificial Intelligence (AAAI Press, Menlo Park 2002), p.93−98.

Google Scholar