Abstract
Objective: The main objective of study is to propose a new method of imputation for missing data. The study discuss misclassification rate, out of bag error for simulated and real data. Method: In this article, a new imputation method has been proposed for IN/OUT procedure of Random Forest (RF). The proposed method does not depend on the missing data mechanisms which are the principal advantages of this method. The method was evaluated and compared with non-missing data sets. Findings and Conclusion: It is concluded that the proposed method reduced the Out-of-Bag error and also the misclassification error rates in case of missing values using IN/OUT Procedure of RF and Conventional RF procedure at the different level of missing percentages. The proposed method gives interesting results in case of (5-15)% missing data and after that, the rest of the results are same therefore no need to compute the results for this percentage % of missing values. The most important is that this method was first time developed in the IN/OUT procedure of RF and conventional RF. Novelty/Motivation: Missing values a serious problem for all statistical problems. RF and IN/ OUT RF are not exception. Therefore a bootstrap based method to impute missing value in the IN/OUT RF was developed. Keywords: Classification and Regression Tree (CART), Misclassification, Out of Bag (OoB), Random Forest (RF)
Highlights
Intelligent data analysis techniques are useful for better investigate real-world data sets
These procedures effectively applied with distinct parametric models such as Gaussian regression and log-linear models. Their usefulness has yet to be demonstrated for treebased models, such as Classification and Regression Trees (CART) and Random Forest which is usually considered as non-parametric methods
In the library of Random Forest (RF) the choice of “na. roughfix” is apply i.e. the column median is used for missing values for the numerical type of variable, on the other hand, the most frequent levels are to be used for the missing values in case of factor type variable[14]
Summary
Intelligent data analysis techniques are useful for better investigate real-world data sets. The assumption about the process that ever causes missing data seems to be that each value in the dataset is likely to be missing[3–5]. Most of the statistical and learning tools cannot handle missing values and these are needed to be deleted This deletion process may produce biased estimates as well as loss of huge amount of precious information. The imputation procedure is further divided into several procedures like mean imputation, regression method stochastic regression method, Hot-deck imputation method, all possible value imputation etc. These procedures effectively applied with distinct parametric models such as Gaussian regression and log-linear models. Their usefulness has yet to be demonstrated for treebased models, such as Classification and Regression Trees (CART) and Random Forest which is usually considered as non-parametric methods
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