feature selection
544 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
The CAST package for training and assessment of spatial prediction models in R
One key task in environmental science is to map environmental variables continuously in space or even in space and time.
Fast Genetic Algorithm for feature selection -- A qualitative approximation approach
In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets.
Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection
Two different feature sets were selected and were used to train multiple ML models with regular and adversarial training.
Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study
The case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD.
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach
To this end, we propose a real-world (indoor and outdoor) dataset comprising over 30K pairs of images and events under both low and normal illumination conditions.
Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients
In this study, the performances of nine machine and deep learning algorithms in combination with two widely used feature selection methods were investigated to predict last status representing mortality, ICU requirement, and ventilation days.
Evaluating Fair Feature Selection in Machine Learning for Healthcare
Our approach addresses both distributive and procedural fairness within the fair machine learning context.
Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning
In this study, we present a method for emotion recognition in Virtual Reality (VR) using pupillometry.
Predicting risk of cardiovascular disease using retinal OCT imaging
A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases.
Automated Feature Selection for Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations.
