Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

This paper works on feature weighting approaches for naive Bayes text classifiers. Almost all
existing feature weighting approaches for naive Bayes text classifiers have some defects …

Naïve Bayes is one of the most popular data mining algorithms. Its efficiency comes from the
assumption of attribute independence, although this might be violated in many real-world …

Background The large volume and suboptimal image quality of portable chest X-rays
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …

Background and objective Medical data plays a decisive role in disease diagnosis. The
classification accuracy of high-dimensional datasets is often diminished by several …

Feature selection plays a crucial role in machine learning and data mining, and improves
the performance of learning models by selecting a distinguishing feature subset and …

Abstract The Naïve Bayes has proven to be a tractable and efficient method for classification
in multivariate analysis. However, features are usually correlated, a fact that violates the …

Wrapper-based feature subset selection (FSS) methods tend to obtain better classification
accuracy than filter methods but are considerably more time-consuming, particularly for …

Unsupervised feature selection (UFS) aims to remove the redundant information and select
the most representative feature subset from the original data, so it occupies a core position …

The movement of cash flow transactions by either electronic channels or physically created
openings for the influx of counterfeit banknotes in financial markets. Aided by global …