16 papers · ranked by Valyu relevance
Xiaohong Wang, Yidi He, Lizhi Wang
In this study, due to the redundant and irrelevant features contained in the multi-dimensional feature parameter set, the information fusion performance of the subspace learning algorithm was reduced. To solve the above problem, a mutual information (MI) and fractal dimension-based unsupervised feature parameters…
Uroš Mlakar, Iztok Fister Jr., Iztok Fister, Heming Jia
Feature selection is essential for enhancing classification accuracy, reducing overfitting, and improving interpretability in high-dimensional datasets. Evolutionary Feature Selection (EFS) methods employ a threshold parameter $θ$ to decide feature inclusion, yet the widely used static setting $θ=0.5$ may not yield…
Yosef Masoudi-Sobhanzadeh, Habib Motieghader, Ali Masoudi-Nejad
Background Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which…
C. Fernandez-Lozano, C. Canto, M. Gestal, J. M. Andrade-Garda + 3 more
'J. R. Rabuñal' 'J. Dorado' 'A. Pazos'] Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms…
Hossam M. Zawbaa, E. Emary, Crina Grosan, Josh Bongard
Nature-inspired heuristics, such as genetic algorithms, genetic programming, ant colony optimization, and particle swarm optimization, have been successfully used for feature selection. GA uses the accuracy of classification as a fitness (objective) function and removes or adds a feature according to the ranking…
Mohamed Ghetas, Mohamed Abd Elaziz, Mohamed Issa
The presence of noisy, redundant, and irrelevant features in high-dimensional datasets significantly degrades the performance of classification models. Feature selection is a critical pre-processing step to mitigate this issue by identifying an optimal feature subset. While the Generalized Normal Distribution…
Muhammad Umar Chaudhry, Jee-Hyong Lee
Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature…
Nicolas Ngo, Pierre Michel, Roch Giorgi
\usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{\gamma }$$\end{document} γ -metric Authors: ['Nicolas Ngo' 'Pierre Michel' 'Roch Giorgi'] Background The…
Firuz Kamalov, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes + 1 more
'Murodbek Safaraliev'] It has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. To address this issue, we propose a highly effective approach, called Nested Ensemble…
Demeke Endalie, Getamesay Haile, Wondmagegn Taye Abebe, Yilun Shang
Text classification is the process of categorizing documents based on their content into a predefined set of categories. Text classification algorithms typically represent documents as collections of words and it deals with a large number of features. The selection of appropriate features becomes important when the…
Jamshid Pirgazi, Mohsen Alimoradi, Tahereh Esmaeili Abharian, Mohammad Hossein Olyaee
'Mohammad Hossein Olyaee'] Feature selection problem is one of the most significant issues in data classification. The purpose of feature selection is selection of the least number of features in order to increase accuracy and decrease the cost of data classification. In recent years, due to appearance of…
Shanshan Xie, Yan Zhang, Danjv Lv, Xu Chen + 2 more
Feature selection plays a very significant role for the success of pattern recognition and data mining. Based on the maximal relevance and minimal redundancy (mRMR) method, combined with feature subset, this paper proposes an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on…
Xiling Liu, Shuisheng Zhou, Miguel Ángel López Guerrero, Juan Luis García Guirao
'Juan Luis García Guirao'] Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are…
Omar A. M. Salem, Feng Liu, Yi-Ping Phoebe Chen, Xi Chen
The main challenge of classification systems is the processing of undesirable data. Filter-based feature selection is an effective solution to improve the performance of classification systems by selecting the significant features and discarding the undesirable ones. The success of this solution depends on the…
Filip Koprivec, Klemen Kenda, Beno Šircelj
In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as…
Jaesung Lee, Jaegyun Park, Hae-Cheon Kim, Dae-Won Kim
Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature…