Results 11 to 20 of about 102,167 (290)

Combining feature ranking algorithms through rank aggregation [PDF]

The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
The problem of combining multiple feature rankings into a more robust ranking is investigated. A general framework for ensemble feature ranking is proposed, alongside four instantiations of this framework using different ranking aggregation methods. An empirical evaluation using 39 UCI datasets, three different learning algorithms and three different ...
Prati, Ronaldo C., Ronaldo C. Prati
openaire   +2 more sources

Feature Selection with the Boruta Package

Journal of Statistical Software, 2010
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 Random Forest classification algorithm.
Miron B. Kursa, Witold R. Rudnicki
doaj   +1 more source

Combining Multiple Feature-Ranking Techniques and Clustering of Variables for Feature Selection

IEEE Access, 2019
Feature selection aims to eliminate redundant or irrelevant variables from input data to reduce computational cost, provide a better understanding of data and improve prediction accuracy.
Anwar Ul Haq, Defu Zhang, He Peng, Sami Ur Rahman   +3 more
doaj   +3 more sources

Feature ranking for multi-target regression [PDF]

Machine Learning, 2019
This paper considers multi-task regression (MTR) where the goal is to learn a model that predicts several target variables simultaneously. In particular the authors address the task of feature ranking to score the importance of descriptive attributes. While there is several work on feature ranking in single-task regression, this paper presents one of ...
Matej Petković, Dragi Kocev, Sašo Džeroski   +2 more
exaly   +3 more sources

Efficient Feature Ranking and Selection Using Statistical Moments

IEEE Access
Unsupervised feature selection methods can be more efficient than supervised methods, which rely on the expensive and time-consuming data labeling process.
Yael Hochma, Yuval Felendler, Mark Last
doaj   +2 more sources

Feature Importance Ranking for Deep Learning

CoRR, 2020
Accepted by NeurIPS 2020, 5 Figures and 1 Table in Main text, 10 Figures and 5 Tables in Supplementary ...
Wojtas, Maksymilian, Chen, Ke; id_orcid 0000-0001-9457-9364   +1 more
core   +9 more sources

A new representation in genetic programming with hybrid feature ranking criterion for high-dimensional feature selection

Complex & Intelligent Systems
Feature selection is a common method for improving classification performance. Selecting features for high-dimensional data is challenging due to the large search space.
Jiayi Li, Fan Zhang, Jianbin Ma
doaj   +2 more sources

Bias and stability of single variable classifiers for feature ranking and selection [PDF]

Expert Systems With Applications, 2014
Shobeir Fakhraei, Hamid Soltanian-Zadeh, Farshad Fotouhi   +2 more
exaly   +2 more sources

Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset

Computers, 2023
Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance.
László Göcs, Zsolt Csaba Johanyák
doaj   +1 more source

Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques

Computers, 2021
The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state.
Nisha Vishnupant Kimmatkar, B. Vijaya Babu   +1 more
doaj   +1 more source