The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …

Tabular data underpins numerous high-impact applications of machine learning from fraud
detection to genomics and healthcare. Classical approaches to solving tabular problems …

Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …

Machine learning is widely used in practice to produce predictive models for applications
such as image processing, speech and text recognition. These models are more accurate …

We propose an improved random forest classifier that performs classification with a
minimum number of trees. The proposed method iteratively removes some unimportant …

This work introduces a novel four-party honest-majority MPC protocol with active security
that achieves comparable efficiency to equivalent protocols in the same setting, while having …

This article describes a R package Boruta, implementing a novel feature selection algorithm
for finding emph {all relevant variables}. The algorithm is designed as a wrapper around a …

Filter-based feature selection has become crucial in many classification settings, especially
object recognition, recently faced with feature learning strategies that originate thousands of …

In the last decade, major advances have been made in the availability of shared
neuroimaging data, such that there are more than 8,000 shared MRI (magnetic resonance …