Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …

Microarray technology has become an emerging trend in the domain of genetic research in
which many researchers employ to study and investigate the levels of genes' expression in a …

Background Several missing value imputation methods for gene expression data have been
proposed in the literature. In the past few years, researchers have been putting a great deal …

Classifier performance, particularly of instance-based learners such as k-nearest neighbors,
is affected by the presence of noisy data. Noise filters are traditionally employed to remove …

In the era of Big Data, many datasets have a common characteristic, the large number of
features. As a result, selecting the relevant features and ignoring the irrelevant and …

Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer.
The selection of biomarker genes from these profiles is significant and crucial for cancer …

It is recognized the importance of knowing the descriptive properties of a dataset when
tackling a data science problem. Having information about the redundancy, complexity and …

Nowadays, a lot of new classification and clustering techniques have been proposed for
microarray data analysis. However, the multiclass microarray data classification is still …