Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

Computational analysis methods including machine learning have a significant impact in the
fields of genomics and medicine. High-throughput gene expression analysis methods such …

For most bioinformatics statistical methods, particularly for gene expression data
classification, prognosis, and prediction, a complete dataset is required. The gene sample …

Incomplete instances with various missing values in real-world scenes have brought
challenges to the classification tasks. Many existing methods impute the incomplete …

Microarray data analysis needs utmost care as it plays a significant role in cancer study. Due
to the excessive complexity of the data extraction process, it loses some relevant information …

Irradiance prediction is a vital task in the renewable energy field. Its aim is to forecast the
irradiance or power of a photovoltaic plant and thus provide a reference for stabilizing the …

The work presented in this paper aims to develop new imputation methods to better handle
missing values encountered in astronomical data analysis, especially the classification of …

As part of celebrating the success of OWA operators and their contributions over the past
decades, this work presents an original investigation of exploiting OWA in dealing with …

It is commonly agreed that the quality of data analysis may be degraded by the presence of
missing data. In various domains such as bioinformatics, an effective tool is required for the …

In the era of big data, a significant amount of data is produced in many applications areas.
However due to various reasons including sensor failures, communication failures …