Computational intelligence for missing data imputation, by Tshilidzi Marwala

By Tshilidzi Marwala

Lately, the difficulty of lacking info imputation has been greatly explored in details engineering.

Computational Intelligence for lacking information Imputation, Estimation, and administration: wisdom Optimization strategies offers equipment and applied sciences in estimation of lacking values given the saw information. supplying a defining physique of study worthwhile to these fascinated about the sphere of analysis, this booklet covers options reminiscent of radial foundation features, aid vector machines, and relevant part research.

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An issue of the criticality of identifying the correct model that describes inter-relationships and rules that govern the data is solved by using the scaled conjugate gradient optimization method and model selection process through cross-validation (Bishop, 1995). BACKGROUND Missing data generates various problems in numerous applications that depend on access to accurate and complete data. Missing Data Methods that handle missing data have been areas of research in statistics, mathematics and other disciplines (Yuan, 2000; Allison, 2000; Rubin, 1978; Roth, 1994; Abdella, 2005; Abdella & Marwala, 2005; Nelwamondo & Marwala, 2007a; Nelwamondo & Marwala, 2008).

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1996). Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review (Los Alamos Tech. Rep. LA-13070-MS). New Mexico, USA: Los Alamos National Laboratory. Donders, R. , van der Heijden, G. J. M. , & Moons, K. G. M. (2006). Review: A gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59(10), 1087-1091. Ewins, D. J. (1986). Modal testing: Theory and practice. Letchworth, UK: Research Studies Press.

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