A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation

The Jackknife, the Bootstrap and Other Resampling Plans

Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120

Advances in kernel methods: support vector learning

Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809

Cumulated gain-based evaluation of IR techniques

Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841

Making Large-Scale SVM Learning PracticalPreprint

Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517

Bayesian Inference in Statistical Analysis.

Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,853

Bayesian Inference in Statistical Analysis

Hill, B. M., Box, G. E. P., & Tiao, G. C. (1974) · Technometrics · doi:10.2307/1267683 · Cited by 1,860

Using statistical testing in the evaluation of retrieval experiments

Hull, D. A. (1993) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/160688.160758 · Cited by 525

More accurate tests for the statistical significance of result differences

Yeh, A. (2000) · Proceedings of the 18th conference on Computational linguistics - · doi:10.3115/992730.992783 · Cited by 378

Statistical inference in retrieval effectiveness evaluation

Savoy, J. (1997) · Information Processing & Management · doi:10.1016/s0306-4573(97)00027-7 · Cited by 161

A Statistical Analysis of the TREC-3 Data

Tague‐Sutcliffe, J., & Blustein, J. (1995) · Cited by 87

A heterogeneous implementation for plant disease identification using deep learning

Sanida, T., Tsiktsiris, D., Sideris, A., & Dasygenis, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12461-7 · Cited by 25

Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial Intelligence

Pacheco, F. A. L., Hermosilla, G., Bustamante, O. P., Villavicencio, G. E., Allende‐Cid, H., Palma, J., Valenzuela, P., García, J., Carpanetti, A., Minatogawa, V., et al. (2022) · Mathematics · doi:10.3390/math10234396 · Cited by 24

A Niching Regression Adaptive Memetic Algorithm for Multimodal Optimization of the Euclidean Traveling Salesman Problem

Jian, S.-J., & Hsieh, S. (2022) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3211954 · Cited by 23

Weighted doubly regularized support vector machine and its application to microarray classification with noise

Li, J., Wang, Y., Cao, Y., & Xu, C. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.08.002 · Cited by 23

Machine Learning Approach to Detect Faults in Anchor Rods of Power Transmission Lines

Barbosa, D. C. P., Tarragô, V. L., de Medeiros, L. H. A., de Melo, M. T., Novo, L. R. G. S. L., Coutinho, M. S., Alves, M. M., Neto, H. B. D. T. L., Gama, P. H. R. P., & dos Santos, R. G. M. (2019) · IEEE Antennas and Wireless Propagation Letters · doi:10.1109/lawp.2019.2932052 · Cited by 23

Tailored Algorithms for Anomaly Detection in Photovoltaic Systems

Branco, P., Gonçalves, F., & Costa, A. C. (2020) · Energies · doi:10.3390/en13010225 · Cited by 23

Multiscale Residual Attention Network for Distinguishing Stationary Humans and Common Animals Under Through-Wall Condition Using Ultra-Wideband Radar

Ma, Y., Qi, F., Wang, P., Liang, F., Lv, H., Yu, X. L., Li, Z., Xue, H., Wang, J., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3006834 · Cited by 23

A novel approach to diagnose sleep apnea using enhanced frequency extraction network

Wu, Y., Pang, X., Zhao, G., Yue, H., Lei, W., & Wang, Y. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106119 · Cited by 23

Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

Chen, Q., Wang, X., Hang, M., & Li, J. (2021) · Open Geosciences · doi:10.1515/geo-2020-0266 · Cited by 23

Regional-CNN-based enhanced Turkish sign language recognition

Yırtıcı, T., & Yurtkan, K. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-021-02082-2 · Cited by 23

WCE‐DCGAN: A data augmentation method based on wireless capsule endoscopy images for gastrointestinal disease detection

Xiao, Z., Lü, J., Wang, X., Li, N., Wang, Y., & Zhao, N. (2022) · IET Image Processing · doi:10.1049/ipr2.12704 · Cited by 23

Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests

Lin, L., Hao, Z., Post, C. J., & Mikhailova, E. A. (2023) · Forests · doi:10.3390/f14020233 · Cited by 23

Detectron2 for Lesion Detection in Diabetic Retinopathy

Chincholi, F., & Koestler, H. (2023) · Algorithms · doi:10.3390/a16030147 · Cited by 23

Nowcasting of fecal coliform presence using an artificial neural network

Pras, A., & Mamane, H. (2023) · Environmental Pollution · doi:10.1016/j.envpol.2023.121484 · Cited by 24

Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies

Dong, Y., Iammarino, M. A., Liu, J., Codling, J., Fagert, J., Mirshekari, M., Lowes, L. P., Zhang, P., & Noh, H. Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-60034-5 · Cited by 23

Inventario de Humedales para Protección Costera (IHPC): una herramienta para la gestión costera en Costa Rica

Calleja, F., & López-Arias, F. (2022) · Ingeniería · doi:10.15517/ri.v32i2.49060 · Cited by 22

SHAP-Driven Feature Analysis Approach for Epileptic Seizure Prediction

Hasan, M., Wu, W., & Zhao, X. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02211-1 · Cited by 22

Dynamic-weighted simplex strategy for learning enabled cyber physical systems

Ramakrishna, S., Hartsell, C., Burruss, M. P., Karsai, G., & Dubey, A. K. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101760 · Cited by 22

Performance Indicator Survey for Object Detection

Park, I., & Kim, S. (2020) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas50221.2020.9268228 · Cited by 22

DDoS Attack Detection in IoT Networks Using Deep Learning Models Combined with Random Forest as Feature Selector

Farukee, M. B., Shabit, M. S. Z., Haque, M. R., & Sattar, A. H. M. S. (2021) · Communications in computer and information science · doi:10.1007/978-981-33-6835-4_8 · Cited by 24

Fast semi-supervised evidential clustering

Antoine, V., Guerrero, J. A., & Xie, J. (2021) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2021.03.008 · Cited by 22

Real-time prediction of Litho-facies from drilling data using an Artificial Neural Network: A comparative field data study with optimizing algorithms

Agrawal, R., Malik, A., Samuel, R., & Saxena, A. (2021) · Journal of Energy Resources Technology · doi:10.1115/1.4051573 · Cited by 22

On Predicting Sensor Readings With Sequence Modeling and Reinforcement Learning for Energy-Efficient IoT Applications

Laidi, R., Djenouri, D., & Balasingham, I. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3116141 · Cited by 22

Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test

Hussain, S., Raza, Z., Giacomini, G., & Goswami, N. (2021) · Biology · doi:10.3390/biology10101029 · Cited by 22

Synergistic Use of Geospatial Data for Water Body Extraction from Sentinel-1 Images for Operational Flood Monitoring across Southeast Asia Using Deep Neural Networks

Kim, J., Kim, H., Jeon, H., Jeong, S., Song, J., Vadivel, S. K. P., & Kim, D. (2021) · Remote Sensing · doi:10.3390/rs13234759 · Cited by 23

XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation

Nesteruk, S., Illarionova, S., Akhtyamov, T., Shadrin, D., Somov, A., Pukalchik, M., & Oseledets, I. V. (2022) · IEEE Access · doi:10.1109/access.2022.3154709 · Cited by 23

Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM

Shah, S. A. A., Masood, M. A., & Yasin, A. (2022) · IEEE Access · doi:10.1109/access.2022.3206539 · Cited by 27

Airborne Multispectral Imagery and Deep Learning for Biosecurity Surveillance of Invasive Forest Pests in Urban Landscapes

Carnegie, A. J., Eslick, H., Barber, P. A., Nagel, M., & Stone, C. (2022) · Urban forestry & urban greening · doi:10.1016/j.ufug.2023.127859 · Cited by 22

A comparative analysis of Indian sign language recognition using deep learning models

Saini, B., Venkatesh, D., Chaudhari, N., Shelake, T., Gite, S., & Pradhan, B. (2023) · Forum for Linguistic Studies · doi:10.18063/fls.v5i1.1617 · Cited by 24

A Deep Learning Framework for the Classification of Brazilian Coins

Swain, D., Rupapara, V., Nour, A. A., Satapathy, S. K., Acharya, B., Mishra, S., & Bostani, A. (2023) · IEEE Access · doi:10.1109/access.2023.3321428 · Cited by 22

Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms

Li, H., Nyirandayisabye, R., Dong, Q., Niyirora, R., Hakuzweyezu, T., Zardari, I. A., & Nkinahamira, F. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.134867 · Cited by 22

A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features

Carnevali, D., Zhong, L., González-Almela, E., Viana, C., Rotkevich, M., Wang, A., Franco-Barranco, D., González-Marfil, A., Neguembor, M. V., Castells-García, Á., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00883-x · Cited by 22

Hair Segmentation Using Heuristically-Trained Neural Networks

Guo, W., & Aarabi, P. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2614653 · Cited by 22

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution

Breitenstein, M. K., Liu, H., Maxwell, K. N., Pathak, J., & Zhang, R. (2017) · Clinical and Translational Science · doi:10.1111/cts.12514 · Cited by 22

Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks

Bighamian, R., Wong, Y. T., Pesaran, B., & Shanechi, M. M. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab225b · Cited by 21

An improved algorithm of white matter hyperintensity detection in elderly adults

Ding, T., Cohen, A. D., O’Connor, E. E., Karim, H. T., Crainiceanu, A., Muschelli, J., López, O. L., Klunk, W. E., Aizenstein, H. J., Krafty, R. T., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102151 · Cited by 21

A Case Study of Machine Learning Classifiers for Real-Time Adaptive Resolution Prediction in Video Coding

Bhat, M., Thiesse, J.-M., & Callet, P. L. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102934 · Cited by 22

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Shaw, S. S., Ahmed, S., Malakar, S., García‐Hernández, L., Abraham, A., & Sarkar, R. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00314-z · Cited by 22

Computing Precision and Recall with Missing or Uncertain Ground Truth

Lamiroy, B., & Sun, T. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-36824-0_15 · Cited by 22

SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EMPreprint

Wagner, T., Merino, F., Stabrin, M., Moriya, T., Antoni, C., Apelbaum, A., Hagel, P., Sitsel, O., Raisch, T., Prumbaum, D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/356584 · Cited by 23

Dynamic Muscle Fatigue State Recognition Based on Deep Learning Fusion Model

Liu, J., Tao, Q., & Wu, B. (2023) · IEEE Access · doi:10.1109/access.2023.3309741 · Cited by 24

PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers

Puchalski, R., Ha, Q. P., Giernacki, W., Nguyen, H. A. D., & Nguyen, L. V. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02101-7 · Cited by 21

A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson’s Disease Detection from Electroencephalogram Signals

Bera, S., Geem, Z. W., Cho, Y. I., & Singh, P. K. (2025) · Diagnostics · doi:10.3390/diagnostics15060773 · Cited by 24

SRAF Insertion via Supervised Dictionary Learning

Geng, H., Zhong, W., Yang, H., Ma, Y., Mitra, J., & Yu, B. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2943568 · Cited by 21

Random walk based distributed representation learning and prediction on Social Networking Services

Li, J., Wu, L., Hong, R., & Hou, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.10.045 · Cited by 21

Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3+

Hadinata, P. N., Simanta, D., Eddy, L., & Nagai, K. (2021) · Journal of the Civil Engineering Forum · doi:10.22146/jcef.65288 · Cited by 21

Neural labeled LDA: a topic model for semi-supervised document classification

Wang, W., Guo, B., Shen, Y., Han, Y., Chen, Y., & Suo, X. (2021) · Soft Computing · doi:10.1007/s00500-021-06310-2 · Cited by 20

Adaptive windowing based recurrent neural network for drift adaption in non-stationary environment

Suryawanshi, S. S., Goswami, A., Patil, P. R., & Mishra, V. K. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04116-0 · Cited by 23

Possibilistic fuzzy c-means with partial supervision

Antoine, V., Guerrero, J. A., & Romero, G. (2022) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2022.08.003 · Cited by 21

Towards reliable monitoring of grass species in nature conservation: Evaluation of the potential of UAV and PlanetScope multi-temporal data in the Central European tundra

Kupková, L., Červená, L., Potůčková, M., Lysák, J., Roubalová, M., Hrázský, Z., Březina, S., Epstein, H. E., & Müllerová, J. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113645 · Cited by 21

Developing an Intelligent Decision Support System for large-scale smart grid communication network planning

Mochinski, M. A., Biczkowski, M., Chueiri, I. J., Jamhour, E., Zambenedetti, V. C., Pellenz, M. E., & Enembreck, F. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111159 · Cited by 21

RASW: A run-time adaptive sliding window to improve Viola-Jones object detection

Comaschi, F., Stuijk, S., Basten, T., & Corporaal, H. (2013) · International Conference on Distributed Smart Cameras · doi:10.1109/icdsc.2013.6778224 · Cited by 21

Mining place-matching patterns from spatio-temporal trajectories using complex real-world places

Bermingham, L., & Lee, I. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.027 · Cited by 21

Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data

Benson, R., Hu, M., Chen, A., Nag, S., Zhu, S., & Conway, M. (2020) · JMIR Public Health and Surveillance · doi:10.2196/19975 · Cited by 21

NEDetector: Automatically extracting cybersecurity neologisms from hacker forums

Li, Y., Cheng, J., Huang, C., Chen, Z., & Niu, W. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102784 · Cited by 24

Conceptual data sampling for breast cancer histology image classification

Rezk, E., Awan, Z., Islam, F., Jaoua, A., Al-Maadeed, S. A., Zhang, N., Das, G., & Rajpoot, N. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.07.018 · Cited by 21

Automated Lung and Colon Cancer Classification Using Histopathological Images

Ji, J., Zhang, W., 耿义群, Wang, H., Wang, C., Dong, Y., Lin, R., Chen, Z., Huang, J., Hong, L., et al. (2024) · Biomedical Engineering and Computational Biology · doi:10.1177/11795972241271569 · Cited by 24

Joint Augmentation and Part Learning for Unsupervised Clothing Change Person Re-Identification

Pang, Z., Zhao, L., Liu, Y., Gaurav Sharma, A., & Wang, C. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3550063 · Cited by 20

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Ahongshangbam, J., Röll, A., Ellsäßer, F., Hendrayanto, H., & Hölscher, D. (2020) · Remote Sensing · doi:10.3390/rs12040651 · Cited by 20

Mobile Hyperspectral Imaging for Material Surface Damage Detection

Aryal, S., Chen, Z., & Tang, S. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000934 · Cited by 20

Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers

Libiseller-Egger, J., Phelan, J. E., Campino, S. G., Mohareb, F. R., & Clark, T. G. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008518 · Cited by 20

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer

Xie, Z., Li, L., & Xu, X. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720821995000 · Cited by 21

Intraoperative assessment of canine soft tissue sarcoma by deep learning enhanced optical coherence tomography

Ye, Y., Sun, W. W., Xu, R. X., Selmic, L. E., & Sun, M. (2021) · Veterinary and Comparative Oncology · doi:10.1111/vco.12747 · Cited by 23

Transfer Component Analysis for Compensation of Temperature Effects on the Impedance-Based Structural Health Monitoring

da Silva, S., Yano, M. O., & Gonsalez, C. G. (2021) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-021-00794-6 · Cited by 20

Speaker identification in stressful talking environments based on convolutional neural network

Shahin, I. M. A., Nassif, A. B., & Hindawi, N. A. A. (2021) · International Journal of Speech Technology · doi:10.1007/s10772-021-09869-1 · Cited by 20

MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification

Lhoest, L., Lamrini, M., Vandendriessche, J., Wouters, N., da Silva, B., Chkouri, M. Y., & Touhafi, A. (2021) · Applied Sciences · doi:10.3390/app11188394 · Cited by 20

An optimal feature based network intrusion detection system using bagging ensemble method for real-time traffic analysis

Chowdhury, R., Sen, S., Roy, A., & Saha, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12330-3 · Cited by 20

A Novel Method for Detecting and Delineating Coppice Trees in UAV Images to Monitor Tree Decline

Ghasemi, M., Latifi, H., & Pourhashemi, M. (2022) · Remote Sensing · doi:10.3390/rs14235910 · Cited by 20

Detection of Damaged Buildings Using Temporal SAR Data with Different Observation Modes

Kim, M., Park, S.-E., & Lee, S. (2023) · Remote Sensing · doi:10.3390/rs15020308 · Cited by 20

A multi-task learning model for non-intrusive load monitoring based on discrete wavelet transform

Luo, J., Liu, S., Cai, Z., Xiong, C., & Tu, G. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-022-05000-6 · Cited by 20

Cataract disease classification from fundus images with transfer learning based deep learning model on two ocular disease datasets

Çetıner, H. (2023) · Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.17714/gumusfenbil.1168842 · Cited by 21

Comparison of machine learning models within different spatial resolutions for predicting the bigeye tuna fishing grounds in tropical waters of the Atlantic Ocean

Song, L., Li, T., Zhang, T., Sui, H., Li, B., & Zhang, M. (2023) · Fisheries Oceanography · doi:10.1111/fog.12643 · Cited by 23

Leveraging High-Resolution Long-Wave Infrared Hyperspectral Laboratory Imaging Data for Mineral Identification Using Machine Learning Methods

Hamedianfar, A., Laakso, K., Middleton, M., Törmänen, T., Köykkä, J., & Torppa, J. (2023) · Remote Sensing · doi:10.3390/rs15194806 · Cited by 20

UAV Detection Using Reinforcement Learning

AlKhonaini, A., Sheltami, T., Mahmoud, A. S. H., & Imam, M. (2024) · Sensors · doi:10.3390/s24061870 · Cited by 20

Monitoring the spatial–temporal distribution of invasive plant in urban water using deep learning and remote sensing technology

Hao, Z., Lin, L., Post, C. J., & Mikhailova, E. A. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112061 · Cited by 23

Segmentation of acute ischemic stroke lesions based on deep feature fusion

Li, L., Liu, J., Chen, S., Wang, J., Li, Y., Liao, Q., Zhang, L., Peng, X., & Pu, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102724 · Cited by 20

A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine

Shan, W., Cai, S., & Liu, C. (2018) · Water · doi:10.3390/w10101303 · Cited by 20

Community detection in complex networks using edge-deleting with restrictions

Chen, X., & Li, J. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.12.023 · Cited by 20

Coding Electronic Health Records with Adversarial Reinforcement Path Generation

Wang, S., Ren, P., Chen, Z., Ren, Z., Nie, J., Ma, J., & de Rijke, M. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401135 · Cited by 21

IDS alerts classification using knowledge-based evaluation

Gupta, D., Joshi, P. S., Bhattacharjee, A. K., & Mundada, R. S. (2012) · 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012) · doi:10.1109/comsnets.2012.6151339 · Cited by 20

Individual Tree Crown Detection and Classification of Live and Dead Trees Using a Mask Region-Based Convolutional Neural Network (Mask R-CNN)

Yao, S., Hao, Z., Post, C. J., Mikhailova, E. A., & Lin, L. (2024) · Forests · doi:10.3390/f15111900 · Cited by 19

A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

Yang, J., Liu, K., Wang, M., Zhao, G., Wu, W., & Yue, Q. (2024) · International Journal of Disaster Risk Science · doi:10.1007/s13753-024-00592-4 · Cited by 18

TLNet: A Deep CNN model for Prediction of tomato Leaf Diseases

Mamun, M. A. A., Karim, D. Z., Pinku, S. N., & Bushra, T. A. (2020) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit51783.2020.9392664 · Cited by 19

Detection of Vegetation Encroachment in Power Transmission Line Corridor from Satellite Imagery Using Support Vector Machine: A Features Analysis Approach

Haroun, F. M. E., Deros, S. N. M., Baharuddin, M. Z., & Din, N. M. (2021) · Energies · doi:10.3390/en14123393 · Cited by 24

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Biçici, S., & Zeybek, M. (2021) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981211029645 · Cited by 19

Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing

Wang, H., Tang, J., Xu, P., Chen, R., & Yao, H. (2022) · Land · doi:10.3390/land11050728 · Cited by 19

Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study

Lin, H., Xue, Y., Chen, K., Zhong, S., & Chen, L. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278217 · Cited by 21

Exploring GNN based program embedding technologies for binary related tasks

Guo, Y., Li, P., Luo, Y., Wang, X., & Wang, Z. (2022) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3524610.3527900 · Cited by 19

A CEP-driven framework for real-time news impact prediction on financial markets

Chen, W., Majzoub, A. E., Al-Qudah, I., & Rabhi, F. (2023) · Service Oriented Computing and Applications · doi:10.1007/s11761-023-00358-8 · Cited by 19

Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input

Hou, B. J., Newn, J., Sidenmark, L., Khan, A. A., Bækgaard, P., & Gellersen, H. W. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581201 · Cited by 21

One-Stage Classifiers Based on U-Net and Autoencoder with Attention for Recognition of Neoplasms from Single-Channel Monochrome Computed Tomography Images

Samarin, A., Savelev, A., Toropov, A., Dzestelova, A., Malykh, V. A., Mikhailova, E., & Motyko, A. A. (2023) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661823020128 · Cited by 19

Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

Raj, G. D., & Prabadevi, B. (2023) · IEEE Access · doi:10.1109/access.2023.3333894 · Cited by 20

Enhanced EEG-fNIRS Classification through Concatenated Convolutional Neural Network with Band Analysis

Bunterngchit, C., Wang, J., Chearanai, T., & Hou, Z. (2023) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio58561.2023.10354890 · Cited by 19

IoT-Based Smart Biofloc Monitoring System for Fish Farming Using Machine Learning

Abid, M. A., Amjad, M., Munir, K., Siddique, H. U. R., & Jurcut, A. D. (2024) · IEEE Access · doi:10.1109/access.2024.3384263 · Cited by 29

Clothing-invariant contrastive learning for unsupervised person re-identification

Pang, Z., Zhao, L., & Wang, C. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106477 · Cited by 22

Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images

Alkhaldi, N. A., & Alabdulathim, R. E. (2024) · Applied Sciences · doi:10.3390/app14177795 · Cited by 26

Text analytics based severity prediction of software bugs for apache projects

Kaur, A., & Jindal, S. G. (2019) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-019-00807-8 · Cited by 20

CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts

Yan, X., Ye, Y., Qiu, X., Manic, M., & Yu, H. L. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2939278 · Cited by 19

Kernel truncated regression representation for robust subspace clustering

Zhen, L., Peng, D., Wang, W., & Yao, X. (2020) · Information Sciences · doi:10.1016/j.ins.2020.03.033 · Cited by 18

Semantics-driven Event Clustering in Twitter Feeds

Boom, C. D., Canneyt, S. V., & Dhoedt, B. (2015) · Ghent University Academic Bibliography (Ghent University) · Cited by 19

Computing Precision and Recall with Missing or Uncertain Ground Truth

Lamiroy, B., & Sun, T. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-36824-0_15 · Cited by 22

What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely $F_1$Preprint

Piérard, S. L., Deliège, A., & Droogenbroeck, M. V. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2511.22442

Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models

Yacouby, R., & Axman, D. (2020) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2020.eval4nlp-1.9 · Cited by 858

Extending F1 Metric, Probabilistic Approach

Sitarz, M. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1161 · Cited by 52

Evaluation: From Precision, Recall and F-Factor

Roc, T., & Powers, D. M. W. (2007)