Article
Chemistry, Multidisciplinary
Chang He, Shuo Zhu, Xiaorong Wu, Jiale Zhou, Yonghui Chen, Xiaohua Qian, Jian Ye
Summary: In this study, Raman spectroscopy combined with the variational autoencoder (VAE) was used to analyze and classify tumor subtypes. The VAE successfully downscaled and reduced noise in the Raman spectra, leading to improved discrimination results compared to the original spectra.
Article
Computer Science, Artificial Intelligence
Khalid Y. Aram, Sarah S. Lam, Mohammad T. Khasawneh
Summary: This article introduces the Alternated Sorting Method Genetic Algorithm (ASMGA), which is a hybrid wrapper-filter algorithm for simultaneous feature selection and model selection for Support Vector Machine (SVM) classifiers. ASMGA approximates a set of Pareto optimal feature subsets based on three objectives: cost-sensitive error rate, feature subset size, and Max-Margin Feature Selection (MMFS)-based estimates of feature relevance and redundancy. The proposed algorithm outperforms canonical GA and NSGA-II on benchmark datasets, showing the potential of ASMGA in cost-sensitive feature selection.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Environmental Sciences
Giorgio Morales, John W. Sheppard, Riley D. Logan, Joseph A. Shaw
Summary: The study introduces a dimensionality reduction technique combining filter-based and wrapper-based methods to select useful bands by analyzing collinearity and information entropy among spectral bands, aiming to reduce redundancy and improve processing efficiency of hyperspectral images. Experimental results demonstrate that the proposed method achieves good performance in handling hyperspectral images.
Article
Computer Science, Artificial Intelligence
Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, Mengjie Zhang
Summary: This study analyzes GP-based approaches to skin image classification, which improve the performance of machine learning classification algorithms by constructing features, thereby enhancing diagnostic efficiency and assisting dermatologists in diagnosis.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biochemical Research Methods
Shuaipeng Fei, Lei Li, Zhiguo Han, Zhen Chen, Yonggui Xiao
Summary: This study proposes a novel method to improve the accuracy of wheat yield prediction using hyperspectral data. The method performs well across different growth stages and can assist wheat breeders in making earlier decisions.
Article
Engineering, Electrical & Electronic
Yang Cao, Wei Feng, Yinghui Quan, Wenxing Bao, Gabriel Dauphin, Aifeng Ren, Xiaoguang Yuan, Mengdao Xing
Summary: Remote sensing image change detection is a key technology for monitoring forest windfall damages. Utilizing genetic algorithm for spatial analysis and ensemble learning can improve classification accuracy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Biochemistry & Molecular Biology
Shida He, Xiucai Ye, Tetsuya Sakurai, Quan Zou
Summary: In this study, we developed a dimensionality reduction tool, MRMD3.0, based on the ensemble strategy of link analysis. The tool integrates different feature ranking algorithms to calculate feature importance and uses forward feature search strategy combined with cross-validation to explore proper feature combinations. The latest version added more link-based ensemble algorithms and improved the speed and effects of feature ranking algorithms, providing an interface and charts for feature analysis. An online webserver is also available to assist researchers in data analysis.
JOURNAL OF MOLECULAR BIOLOGY
(2023)
Review
Agriculture, Multidisciplinary
R. Manavalan
Summary: This paper presents a comprehensive computational method for diagnosing cotton leaf diseases to improve productivity by analyzing various stages of the plant-pathogen system. The paper finds that current automated identification methods for cotton crop diseases are still in their infancy and discusses the issues behind the computational approaches of plant pathogens in-depth. The strengths and weaknesses of existing methods are highlighted, and future directions for research are presented. Therefore, there is a need for novel, fully automatic computer-assisted systems to detect and classify numerous diseases in cotton plants.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Mathematics, Interdisciplinary Applications
Dong Yang, Peijian Wu
Summary: This paper explores the optimization of e-commerce logistics and distribution networks, proposing solutions that take into account city traffic conditions and establishing an optimization model through various methods. The study focuses on vehicle path problems, multidimensional impact maximization problems, and proposes solutions for emergency material delivery path planning.
Article
Multidisciplinary Sciences
Zohre Karimi
Summary: The opinion of consumers on an organization's products, services, and events is crucial for businesses, and analyzing these opinions using machine learning methods provides important performance indicators. However, existing text feature representation methods have limitations. This paper proposes a nonlinear feature selection method based on manifold assumption to enhance opinion mining, and presents experimental results on two benchmark datasets.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Yu Zhou, Wenjun Zha, Junhao Kang, Xiao Zhang, Xu Wang
Summary: This paper proposes a problem-specific non-dominated sorting genetic algorithm (PS-NSGA) that can minimize three objectives of feature selection. By applying an accuracy-preferred domination operator and a quick bit mutation, the algorithm converges faster and better, achieving competitive classification accuracy in experiments.
INFORMATION SCIENCES
(2021)
Article
Multidisciplinary Sciences
Masood Ul Hassan, Rakesh Veerabhadrappa, Asim Bhatti
Summary: Neural spike sorting is essential for extracting useful information from electrophysiological data recorded from the brain, but the efficiency of conventional spike sorting algorithms decreases when dealing with large and dense datasets. A novel data pre-processing framework is proposed to enhance the efficiency of these algorithms.
Article
Computer Science, Artificial Intelligence
Fatemeh Amini, Guiping Hu
Summary: A new two-layer feature selection approach incorporating Genetic Algorithm and Elastic Net has been proposed to improve prediction accuracy in machine learning. Experimental results confirm the superiority of the proposed model.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Tansel Dokeroglu, Ayca Deniz, Hakan Ezgi Kiziloz
Summary: This survey examines the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges and possible future research topics are also discussed.
Article
Computer Science, Artificial Intelligence
Izabela Rejer, Jaroslaw Jankowski
Summary: The paper introduces a modified version of a genetic algorithm called fGAAM that significantly decreases the time needed to find feature subsets with satisfactory classification accuracy. The fGAAM was shown to be faster than other genetic algorithms and able to process datasets that other methods could not handle due to their high number of features.
PATTERN ANALYSIS AND APPLICATIONS
(2022)