Problem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we can negle...

in the classical data envelopment analysis (dea) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. in recent years, there are few researches on handling missing data. this paper suggests a new interval based approach to apply missing data, which is the modified version of kousmanen (2009) approach. first, the prop...

Imputing values to missing cases is a subject that frequently met in the fields of Machine Learning and Data Mining, require researchers study it. It known many computer-based analysis algorithms operate under assumption there no case. The lack sufficient search case by able negatively affect performance results. In this study, it was studied with data set consisting 52 variables order measure ...

Abstract Longitudinal datasets of human ageing studies usually have a high volume missing data, and one way to handle values in dataset is replace them with estimations. However, there are many methods estimate values, no single method the best for all datasets. In this article, we propose data-driven value imputation approach that performs feature-wise selection method, using known information...

Missing data is universal complexity for most part of the research fields which introduces uncertainty into analysis. We can take place due to many types motives such as samples mishandling, unable collect an observation, measurement errors, aberrant value deleted, or merely be short study. The nourishment area not exemption difficulty missing. Most frequently, this determined by manipulative m...