Various predictive modeling approaches based on the customers’ information may be used for selecting proper targets for a promoted product to entice customers into purchasers. However, there is a fundamental problem, the incomplete data which can yield biased results and deteriorate the accuracy of those approaches. So far, several methods such as case deletion and mean substitution are applied...

BACKGROUND Targeted screening for cardiovascular disease (CVD) can be carried out using existing data from patient medical records. However, electronic medical records in UK general practice contain missing risk factor data for which values must be estimated to produce risk scores. OBJECTIVE To compare two methods of substituting missing risk factor data; multiple imputation and the use of d...

BACKGROUND Estimations of survival rates are diverse and the choice of the appropriate method depends on the context. Given the increasing interest in multiple imputation methods, we explored the interest of a multiple imputation approach in the estimation of cause-specific survival, when a subset of causes of death was observed. METHODS By using European Randomized Study of Screening for Pro...

OBJECTIVES Many classification problems must deal with data that contains missing values. In such cases data imputation is critical. This paper evaluates the performance of several statistical and machine learning imputation methods, including our novel multiple imputation ensemble approach, using different datasets. MATERIALS AND METHODS Several state-of-the-art approaches are compared using...

We present a framework for generating multiple imputations for continuous data when the missing data mechanism is unknown. Imputations are generated from more than one imputation model in order to incorporate uncertainty regarding the missing data mechanism. Parameter estimates based on the different imputation models are combined using rules for nested multiple imputation. Through the use of s...

In the field of data quality, imputation is the most used method for handling missing data. The performance of imputation techniques is influenced by various factors, especially when data represent only a sample of population, for example the survey design characteristics. In this paper, we compare the results of different multiple imputation methods in terms of final estimates when outliers oc...

Abstract The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complica...

Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertaint...

The treatment of incomplete data is an important step in the pre-processing of data. We propose a novel nonparametric algorithm Generalized regression neural network Ensemble for Multiple Imputation (GEMI). We also developed a single imputation (SI) version of this approach—GESI. We compare our algorithms with 25 popular missing data imputation algorithms on 98 real-world and synthetic terms of...

Missing data imputation is an important research topic in data mining. Large-scale Molecular descriptor data may contains missing values (MVs). However, some methods for downstream analyses, including some prediction tools, require a complete descriptor data matrix. We propose and evaluate an iterative imputation method MiFoImpute based on a random forest. By averaging over many unpruned regres...