Multi-Label Classification Algorithm for Adaptive Heterogeneous Classifier Group by Meng Han, Shurong Yang, Hongxin Wu, Jian Ding

Subjects: “…multi-label classification…”
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Effective and Reliable Malware Group Classification for a Massive Malware Environment by Taejin Lee, Jin Kwak

“…This paper proposes a scheme for the detection and group classification of malware, some measures to improve the dependability of classification using the local clustering coefficient, and the technique for selecting and managing the leading malware for each group to classify them cost-effectively in a massive malware environment. …”
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Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network by Zhenzhe Qin, Yongbo Li, Xiaoying Song, Li Chai

“…For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). …”
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An improved electrocardiogram arrhythmia classification performance with feature optimization by Annisa Darmawahyuni, Siti Nurmaini, Bambang Tutuko, Muhammad Naufal Rachmatullah, Firdaus Firdaus, Ade Iriani Sapitri, Anggun Islami, Jordan Marcelino, Rendy Isdwanta, Muhammad Ikhwan Perwira

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Problem of selection of suitable geosynthetics for the strengthening of subgrade by Elżbieta Szafranko

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Video inter-frame tampering detection algorithm fusing multiple features by Hui XIAO, Bin WENG, Tianqiang HUANG, Han PU, Zehui HUANG

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Metric-Based Few-Shot Learning With Triplet Selection for Adaptive GNSS Interference Classification by Felix Ott, Lucas Heublein, Tobias Feigl, Alexander Rugamer, Christopher Mutschler

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Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction by P. Nancy, Prasad Raghunath Mutkule, Kalpana Sunil Thakre, Ajay S. Ladkat, S.B.G. Tilak Babu, Sunil L. Bangare, Mohd Naved

“…The aim of this work is to provide a method for the prediction and classification of cardiac disease based on machine learning and feature selection. …”
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The Improved Kurdish Dialect Classification Using Data Augmentation and ANOVA-Based Feature Selection by Karzan J. Ghafoor, Sarkhel H. Karim, Karwan M. Hama Rawf, Ayub O. Abdulrahman

“…Analysis of variance (ANOVA) filter approach is applied to improve feature selection (FS) more efficiently and highlight the most relevant features for dialect classification. …”
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Feature Set to sEMG Classification Obtained With Fisher Score by Diana C. Toledo-Perez, Marcos Aviles, Roberto A. Gomez-Loenzo, Juvenal Rodriguez-Resendiz

“…The central purpose of this research is to develop and validate a methodology that uses the Fisher Score to select a set of features in the classification of sEMG signals. …”
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A Feature Selection Approach to the Group Behavior Recognition Issue Using Static Context Information by Alberto Pozo, Miguel A. Patricio, Jesús García, José Manuel Molina

“…This paper deals with the problem of group behavior recognition. Our approach is to merge all the possible features of group behavior (individuals, groups, relationships between individuals, relationships between groups, etc.) with static context information relating to particular domains. …”
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Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery by Sudha GC Upadhaya, Chongyuan Zhang, Sindhuja Sankaran, Timothy Paulitz, David Wheeler

“…Although this work utilized only three of the nearly eight known VCGs, the findings underscore the potential of the HSI for fungal group classification. The study also highlights the need for future work to include a wider range of VCGs from multiple regions, larger sample sizes, and careful selection of feature sets to enhance model performance and generalizability.…”
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IMPROVING THE ACCURACY OF DETERMINING DEVIANT GROUPS IN THE SELECTION AND MONITORING OF THE CRITICAL INFORMATION INFRASTRUCTURE ENTERPRISES STAFF by Vladimir L. Evseev, Anton S. Burakov, Anatoly V. Marchenko

“…The dependence of k-means clustering on the selection of initial cluster centers is substantiated, affecting the accuracy of grouping critical information infrastructure enterprise employees by their multidimensional behavioral characteristics, which leads to critical errors in their classification. …”
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Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose. by Jia Liu, Jinglei Zhang, Shaoqi Zhang, Kaiwei Li, Xiang Li, Shuo Zhang, Hang Gu, Zhen Chen, Chao Liu, Nan Zhang, Tong Sun

“…For this issue, we proposed a new sensor array optimization algorithm named Interfered Feature Elimination coupled with Feature Group Selection (IFE-FGS). In this method, the IFE algorithm first removed the bad sensor features; then the FGS algorithm determined the optimized sensor combination by gradually selecting the features in groups. …”
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Optimized Time Series Feature Selection for Manufacturing AI: Reducing Complexity and Improving Classification Accuracy by Jaeseok Jang, Chanyoung Jung, Hail Jung

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GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification by Junding Sun, Haoxiang Dong, Yanlong Gao, Xiaosheng Wu, Jianlong Wang, Yudong Zhang

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Improving crop rotation classification using a random forest model incorporating spatial heterogeneity by Xiaomi Wang, Qi Tang, Kang Yang

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EVALUATION AND VARIABILITY OF LINEAR CLASSIFICATION INDICATORS IN THEIR RELATIONSHIP WITH MILK YIELD OF COWS OF HOLSTEIN BREED OF REGIONAL SELECTION by Leontii KHMELNYCHYI, Bohdan KARPENKO

“…Researches have been conducted to study conformation type of firstborn Holstein breed cows of Ukrainian selection. Cows at the age of first lactation were evaluated in the period of 2-4 months of its course using two systems of linear classification as recommended by ICAR. …”
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Sentinel-2 image based smallholder crops classification and accuracy assessment by UAV data by Kadierye Maolan, Yusufujiang Rusuli, Zhang XuHui, Yimuran Kuluwan

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Intelligent intrusion detection system based on crowd search optimization for attack classification in network security by Chetan Gupta, Amit Kumar, Neelesh Kumar Jain

“…We have also created a confusion matrix over different kinds of attacks concerning predicted class and attack class, enabling us to evaluate the performance of a classification model for various attack groups without taking CSO and after applying the CSO approach. …”
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