By Tshilidzi Marwala
Lately, the problem of lacking info imputation has been commonly explored in info engineering.
Computational Intelligence for lacking facts Imputation, Estimation, and administration: wisdom Optimization strategies offers equipment and applied sciences in estimation of lacking values given the saw facts. supplying a defining physique of study important to these excited about the sphere of research, this e-book covers recommendations equivalent to radial foundation capabilities, help vector machines, and important part research.
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Lately, the difficulty of lacking information imputation has been generally explored in info engineering. Computational Intelligence for lacking facts Imputation, Estimation, and administration: wisdom Optimization thoughts offers tools and applied sciences in estimation of lacking values given the saw information.
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Extra resources for Computational intelligence for missing data imputation, estimation and management: knowledge optimization techniques
It can be viewed as an extension of the simple-rule prediction. The distinction between this class and the preceding one is that, in place of using one variable, the multivariate rule prediction employs more than one variable. Neural networks imputation can be viewed as one example of this class (Nelwamondo, 2008). This technique is also known as Multiple Imputation (MI), and was introduced by Rubin (1987). It merges statistical techniques by producing a maximum-likelihood based covariance matrix and a vector of means.
T. H. (2005). Effective techniques for handling incomplete data using decision trees. Unpublished doctoral dissertation, The Open University, UK. Velilla, S. (1993). On eigenvalues, case deletion and extremes in regression. Computational Statistics & Data Analysis, 16(3), 299-309. Wang, S. (2005). Classification with incomplete survey data: a Hopfield neural network approach. Computers & Operations Research, 24, 53–62. , & Mirkin, B. (2006). Nearest neighbors in least-squares data imputation algorithms with different missing patterns.
The models built for missing data estimation normally assume the traditional manner of quantifying uncertainty using probability models. Recently, possibility and plausibility models have been developed using fuzzy logic and rough sets. Therefore, how do we use these developments to tackle difficult problems that contain subjective observations? How do we exploit statistical machine learning tools such as support vector machines to deal with large multivariate data? How do we merge computational intelligence with decision-trees to decrease the search space in pursuit for the missing values?
Computational intelligence for missing data imputation, estimation and management: knowledge optimization techniques by Tshilidzi Marwala