The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...
Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with o...
Microarray data often contains multiple missing genetic expression values that degrade the performance of statistical and machine learning algorithms. This paper presents a K rank...
Muhammad Shoaib B. Sehgal, Iqbal Gondal, Laurence ...
— Imputation of missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical documentation. Estimating the uncertainty inherent ...
Effort prediction is a very important issue for software project management. Historical project data sets are frequently used to support such prediction. But missing data are oft...