The accurate prediction of heart disease is essential to efficiently treating cardiac patients
before a heart attack occurs. This goal can be achieved using an optimal machine learning …

In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

Data is essential for an organization to develop and make decisions efficiently and
effectively. Machine learning classification algorithms are used to categorize observations …

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms, but its
conditional independence assumption rarely holds true in real-world applications …

The outbreak of Coronavirus (COVID-19) has spread between people around the world at a
rapid rate so that the number of infected people and deaths is increasing quickly every day …

Crowdsourcing provides an efficient way to obtain multiple noisy labels from different crowd
workers for each unlabeled instance. Label integration methods are designed to infer the …

The random forest (RF) classifier, which is a combination of tree predictors, is one of the
most powerful classification algorithms that has been recently applied for fault detection and …

COVID-19, as an infectious disease, has shocked the world and still threatens the lives of
billions of people. Early detection of COVID-19 patients is an important issue for treating and …

We propose a neural network-based feature selection (FS) scheme that can control the level
of redundancy in the selected features by integrating two penalties into a single objective …

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 …