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 real-time stress classification system based on arousal analysis of the nervous system by an F-state machine

Martínez, R., Irigoyen, E., Arruti, A., Martin, J. I., & Muguerza, J. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.06.010 · Cited by 41

Machine Learning based parameter tuning strategy for MMC based topology optimization

Jiang, X., Wang, H., Li, Y., & Mo, K. (2020) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2020.102841 · Cited by 42

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution

Zhang, C., Hua, Q., Chu, Y., & Wang, P. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104424 · Cited by 43

Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment

Zhou, L., Meng, R., Tan, Y., Lv, Z., Zhao, Y., Xu, B., & Zhao, F. (2022) · Urban forestry & urban greening · doi:10.1016/j.ufug.2022.127489 · Cited by 41

Recognition of wheat rusts in a field environment based on improved DenseNet

Chang, S., Yang, G., Cheng, J., Feng, Z., Fan, Z., Ma, X., Li, Y., Yang, X., & Zhao, C. (2024) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2023.12.016 · Cited by 41

Real-Time Continuous Detection and Recognition of Subject-Specific Smart TV Gestures via Fusion of Depth and Inertial Sensing

Dawar, N., & Kehtarnavaz, N. (2018) · IEEE Access · doi:10.1109/access.2017.2788558 · Cited by 41

On Diversity in Image Captioning: Metrics and Methods

Wang, Q., Wan, J., & Chan, A. B. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3013834 · Cited by 43

Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks

Fang, S., Tan, Y., Zhang, T., Xu, Z., & Liu, H. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3074412 · Cited by 43

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Saeed, U., Shah, S. Y., Zahid, A., Ahmad, J. E., Imran, M. A., Abbasi, Q. H., & Shah, S. A. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3096641 · Cited by 41

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Hasan, M. J., Islam, M. M. M., & Kim, J.-M. (2021) · Sensors · doi:10.3390/s22010056 · Cited by 43

cLoops2: a full-stack comprehensive analytical tool for chromatin interactions

Cao, Y., Liu, S., Ren, G., Tang, Q., & Zhao, K. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1233 · Cited by 41

Supervised intentional process models discovery using Hidden Markov models

Khodabandelou, G., Hug, C., Deneckere, R., & Salinesi, C. (2013) · International Conference on Research Challenges in Information Science (RCIS) · doi:10.1109/rcis.2013.6577711 · Cited by 41

Location-Based Reasoning about Complex Multi-Agent Behavior

Sadilek, A., & Kautz, H. (2012) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3421 · Cited by 40

Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition

Chen, M., Holobar, A., Zhang, X., & Zhou, P. (2016) · Neural Plasticity · doi:10.1155/2016/3489540 · Cited by 40

CNN Classification of the Cultural Heritage Images

Ćosović, M., & Janković, R. (2020) · International Symposium INFOTEH-JAHORINA (INFOTEH) · doi:10.1109/infoteh48170.2020.9066300 · Cited by 40

Partial Visual-Tactile Fused Learning for Robotic Object Recognition

Zhang, T., Cong, Y., Dong, J., & Hou, D. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3096235 · Cited by 40

Intelligent transportation systems: A survey on modern hardware devices for the era of machine learning

Damaj, I., Khatib, S. K. A., Naous, T., Lawand, W., Abdelrazzak, Z. Z., & Mouftah, H. T. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.07.020 · Cited by 40

Full-Body Motion Recognition in Immersive- Virtual-Reality-Based Exergame

Caserman, P., Liu, S., & Göbel, S. P. (2021) · IEEE Transactions on Games · doi:10.1109/tg.2021.3064749 · Cited by 39

MIMR: Modality-Invariance Modeling and Refinement for unsupervised visible-infrared person re-identification

Pang, Z., Wang, C., Pan, H., Zhao, L., Wang, J., & Guo, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111350 · Cited by 42

KGIE: Knowledge graph convolutional network for recommender system with interactive embedding

Li, M., Ma, W., & Chu, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111813 · Cited by 42

One-dimensional convolutional neural network and hybrid deep-learning paradigm for classification of specific language impaired children using their speech

Sharma, Y., & Singh, B. K. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106487 · Cited by 38

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

Hou, Y., Zhao, G., Chen, X., & Yu, X. (2022) · Remote Sensing · doi:10.3390/rs14143306 · Cited by 39

Inter-Modality Similarity Learning for Unsupervised Multi-Modality Person Re-Identification

Pang, Z., Zhao, L., Liu, Y., Gaurav Sharma, A., & Wang, C. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3408831 · Cited by 43

Classification methods performance on human activity recognition

Bustoni, I. A., Hidayatulloh, I., Ningtyas, A., Purwaningsih, A., & Azhari, S. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1456/1/012027 · Cited by 38

MDFI: Multi-CNN Decision Feature Integration for Diagnosis of Cervical Precancerous Lesions

Luo, Y., Zhang, T., Li, P., Liu, P., Sun, P., Dong, B., & Ruan, G. (2020) · IEEE Access · doi:10.1109/access.2020.2972610 · Cited by 40

Detection of Hate Speech in Videos Using Machine Learning

Wu, C. S., & Bhandary, U. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00104 · Cited by 38

Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays

Uddin, A., Talukder, B., Khan, M. M., & Zaguia, A. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/3366057 · Cited by 39

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

Ditria, E. M., Sievers, M., Lopez-Marcano, S., Jinks, E. L., & Connolly, R. M. (2020) · Environmental Monitoring and Assessment · doi:10.1007/s10661-020-08653-z · Cited by 41

Unraveling the complexities of urban fluvial flood hydraulics through AI

Mehedi, M. A. A., Smith, V. B., Hosseiny, H., & Jiao, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-23214-9 · Cited by 37

Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging

Amasya, H., Jaju, P. P., Ezhov, M., Gusarev, M., Atakan, C., Sanders, A., Manulius, D., Golitskya, M., Shrivastava, K., Singh, A., et al. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22973 · Cited by 39

A DT-SVM Strategy for Stock Futures Prediction with Big Data

Wang, D., Liu, X., & Wang, M. (2013) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2013.147 · Cited by 38

Individual tree segmentation from a leaf-off photogrammetric point cloud

Carr, J. C., & Slyder, J. B. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1434330 · Cited by 37

Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms

Mollalo, A., Vahedi, B., Bhattarai, S., Hopkins, L. C., Banik, S., & Vahedi, B. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104248 · Cited by 37

Image-Based Machine Learning Characterizes Root Nodule in Soybean Exposed to Silicon

Chung, Y. S., Lee, U., Heo, S., Silva, R. R., Na, C., & Kim, Y.-H. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.520161 · Cited by 37

How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card

Kühl, N., Hirt, R., Baier, L., Schmitz, B., & Satzger, G. (2021) · Communications of the Association for Information Systems · doi:10.17705/1cais.04845 · Cited by 43

An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans

Cruz, J. F. H. S. (2021) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2021.100027 · Cited by 38

Automated retrieval of information on threatened species from online sources using machine learning

Kulkarni, R., & Minin, E. D. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13608 · Cited by 37

Efficient Maize Tassel-Detection Method using UAV based remote sensing

Kumar, A., Desai, S. V., Balasubramanian, V. N., Rajalakshmi, P., Guo, W., Naik, B. B., Balram, M., & Desai, U. B. (2021) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2021.100549 · Cited by 37

Wave-by-wave nearshore wave breaking identification using U-Net

Sáez, F. J., Catalán, P. A., & Valle, C. (2021) · Coastal Engineering · doi:10.1016/j.coastaleng.2021.104021 · Cited by 40

Image Processing Based Approach for False Data Injection Attacks Detection in Power Systems

Moayyed, H., Mohammadpourfard, M., Konstantinou, C., Moradzadeh, A., Mohammadi‐Ivatloo, B., & Aguiar, A. P. (2021) · IEEE Access · doi:10.1109/access.2021.3131506 · Cited by 39

Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model

Fan, Z., Zhan, T., Gao, Z., Li, R., Liu, Y., Zhang, L., Jin, Z., & Xu, S. (2022) · IEEE Access · doi:10.1109/access.2022.3175978 · Cited by 40

Modeling and prediction of fire occurrences along an elevational gradient in Western Himalayas

Bar, S., Parida, B. R., Pandey, A. C., Shankar, B. U., Kumar, P., Panda, S. K., & Behera, M. D. (2022) · Applied Geography · doi:10.1016/j.apgeog.2022.102867 · Cited by 38

LLM-Commentator: Novel fine-tuning strategies of large language models for automatic commentary generation using football event data

Cook, A., & Karakuş, O. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112219 · Cited by 42

Advanced User Credit Risk Prediction Model Using LightGBM, XGBoost and Tabnet with SMOTEENN

Yu, C., Jin, Y., Xing, Q., Zhang, Y., Guo, S.-B., & Meng, S. (2024) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics62053.2024.10796247 · Cited by 36

SPEX2: automated concise extraction of spatial gene expression patterns from Fly embryo ISH images

Puniyani, K., Faloutsos, C., & Xing, E. P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq172 · Cited by 36

Object-Based Morphological Profiles for Classification of Remote Sensing Imagery

Geiß, C., Klotz, M., Schmitt, A., & Taubenböck, H. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2576978 · Cited by 36

Predicting time to graduation at a large enrollment American university

Aiken, J. M., Bin, R. D., Hjorth-Jensen, M., & Caballero, M. D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242334 · Cited by 38

Dendrite morphological neural networks for motor task recognition from electroencephalographic signals

Antelis, J. M., Gudiño-Mendoza, B., Falcón-Morales, L. E., Sanchez‐Ante, G., & Sossa, H. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.03.010 · Cited by 36

Driving behavior assessment: A practical study and technique for detecting a driver's condition and driving style

Karrouchi, M., Nasri, I., Rhiat, M., Atmane, I., Hirech, K., Messaoudi, A., Melhaoui, M., & Kassmi, K. (2023) · Transportation Engineering · doi:10.1016/j.treng.2023.100217 · Cited by 35

A hybrid machine learning-based model for predicting flight delay through aviation big data

Dai, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-55217-z · Cited by 43

Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling

Yu, H., Weng, L., Wu, S., He, J., Yuan, Y., Wang, J., Xu, X., & Feng, X. (2024) · Plant Phenomics · doi:10.34133/plantphenomics.0158 · Cited by 36

Marine oil spill detection and segmentation in SAR data with two steps Deep Learning framework

Trujillo-Acatitla, R., Tuxpan-Vargas, J., Ovando‐Vázquez, C., & Monterrubio-Martínez, E. (2024) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2024.116549 · Cited by 36

Intelligent Syndrome Differentiation of Traditional Chinese Medicine by ANN: A Case Study of Chronic Obstructive Pulmonary Disease

Xu, Q., Tang, W., Teng, F., Peng, W., Zhang, Y., Li, W., Wen, C., & Guo, J. (2019) · IEEE Access · doi:10.1109/access.2019.2921318 · Cited by 35

Avoiding Occupancy Detection From Smart Meter Using Adversarial Machine Learning

Yilmaz, I., & Siraj, A. (2021) · IEEE Access · doi:10.1109/access.2021.3057525 · Cited by 37

An Empirical Study of Developer Discussions in the Gitter Platform

Ehsan, O., Hassan, S., Mezouar, M. E., & Zou, Y. (2020) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3412378 · Cited by 35

Representative feature selection of molecular descriptors in QSAR modeling

Li, J., Luo, D., Wen, T., Liu, Q., & Mo, Z. (2021) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2021.131249 · Cited by 35

Regional-scale burned area mapping in Mediterranean regions based on the multitemporal composite integration of Sentinel-1 and Sentinel-2 data

Luca, G. D., Silva, J. M. N., & Modica, G. (2022) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2128251 · Cited by 35

A new approach for mobile robot localization based on an online IoT system

Dourado, C. M. J. D. M., da Silva, S. P. P., da Nóbrega, R. V. M., Barros, A. C. S., Sangaiah, A. K., Filho, P. P. R., & de Albuquerque, V. H. C. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.074 · Cited by 36

A novel liver cancer diagnosis method based on patient similarity network and DenseGCN

Zhang, G., Peng, Z., Yan, C., Wang, J., Luo, J., & Luo, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-10441-3 · Cited by 34

Putting team formations in association football into context

Bauer, P., Anzer, G., & Shaw, L. (2023) · Journal of Sports Analytics · doi:10.3233/jsa-220620 · Cited by 34

LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI

Kundu, R. K., Islam, R., Quarles, J. P., & Hoque, K. A. (2023) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr55154.2023.00076 · Cited by 34

Extending F1 Metric, Probabilistic Approach

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

An automated behavior analysis system for freely moving rodents using depth image

Wang, Z., Mirbozorgi, S. A., & Ghovanloo, M. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1816-1 · Cited by 34

A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis

Harford, S., Darabi, H., Rios, M. D., Majumdar, S., Karim, F., Hoek, T. L. V., Erwin, K., & Watson, D. P. (2019) · Resuscitation · doi:10.1016/j.resuscitation.2019.03.012 · Cited by 34

Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection

Zemmour, E., Kurtser, P., & Edan, Y. (2019) · Sensors · doi:10.3390/s19092130 · Cited by 34

Comparative Analysis of Machine Learning Algorithms through Credit Card Fraud Detection

Banerjee, R., Bourla, G., Chen, S., Kashyap, M., & Purohit, S. (2018) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc45901.2018.9244782 · Cited by 34

A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions

Hasan, M. J., Sohaib, M., & Kim, J.-M. (2020) · Sensors · doi:10.3390/s20247205 · Cited by 33

An optimized deep learning model using Mutation-based Atom Search Optimization algorithm for cervical cancer detection

Chitra, B., & Kumar, S. S. (2021) · Soft Computing · doi:10.1007/s00500-021-06138-w · Cited by 34

Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information

Wang, T., Lu, C., Ju, W., & Liu, C. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103105 · Cited by 34

Contextual security awareness: A context-based approach for assessing the security awareness of users

Solomon, A., Michaelshvili, M., Bitton, R., Shapira, B., Rokach, L., Puzis, R., & Shabtai, A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108709 · Cited by 34

Statistical inference on recall, precision and average precision under random selection

Zhang, P., & Su, W. (2012) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2012.6234049 · Cited by 36

Attention deficit hyperactivity disorder detection in children using multivariate empirical EEG decomposition approaches: A comprehensive analytical study

Sharma, Y., & Singh, B. K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119219 · Cited by 33

AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN

Oh, S., Lee, M., Lee, H., Bertino, E., & Kim, H. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583473 · Cited by 36

Machine Learning-Based Water Quality Classification Assessment

Chen, W., Xu, D., Pan, B., Zhao, Y. C., & Song, Y. X. (2024) · Water · doi:10.3390/w16202951 · Cited by 38

Video Object Segmentation and Tracking Framework With Improved Threshold Decision and Diffusion Distance

Chien, S., Chan, W.-K., Tseng, Y.-H., & Chen, H.-Y. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2242595 · Cited by 33

Prediction of drivers and pedestrians' behaviors at signalized mid-block Danish offset crosswalks using Bayesian networks

Kutela, B., & Teng, H. (2019) · Journal of Safety Research · doi:10.1016/j.jsr.2019.02.008 · Cited by 33

Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks

Chen, M., Li, W., Lin, Q., Lu, S.-L., & Chen, D. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9107-3 · Cited by 33

Predicting students’ performance in English and Mathematics using data mining techniques

Roslan, M. H. B., & Chen, C. J. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11259-2 · Cited by 33

Multivariate Time Series Classification using Dilated Convolutional Neural NetworkPreprint

Yazdanbakhsh, O., & Dick, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01697 · Cited by 48

Using molluscs to assess ecological quality status of soft-bottom habitats along the Atlantic coastline of the United States

Pruden, M. J., Dietl, G. P., Handley, J. C., & Smith, J. A. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107910 · Cited by 32

Data-Driven Prediction of Janus/Core–Shell Morphology in Polymer Particles: A Machine-Learning Approach

Esteki, B., Masoomi, M., Moosazadeh, M., & Yoo, C. (2023) · Langmuir · doi:10.1021/acs.langmuir.2c03355 · Cited by 32

Automatic suppression of false positive alerts in anti-money laundering systems using machine learning

Bakry, A. N., Alsharkawy, A. S., Farag, M. S., & Raslan, K. R. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05708-z · Cited by 32

Unveiling the efficacy of ChatGPT in evaluating critical thinking skills through peer feedback analysis: Leveraging existing classification criteria

Tang, T., Sha, J., Zhao, Y., Wang, S., Wang, Z., & Shen, S. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101607 · Cited by 52

A bounded neural network for open set recognition

Cardoso, D. O., França, F. M. G., & da Gama, J. M. P. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280680 · Cited by 33

A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation

Xu, X., Matsunawa, T., Nojima, S., Kodama, C., Kotani, T., & Pan, D. Z. (2016) · International Symposium on Physical Design · doi:10.1145/2872334.2872357 · Cited by 32

Machine Learning Prediction of Radiofrequency Thermal Ablation Efficacy: A New Option to Optimize Thyroid Nodule Selection

Negro, R., Rucco, M., Creanza, A., Mormile, A., Limone, P. P., Garberoglio, R., Spiezia, S., Monti, S., Cugini, C., Dalati, G. E., et al. (2019) · European Thyroid Journal · doi:10.1159/000504882 · Cited by 32

DistPreserv: Maintaining User Distribution for Privacy-Preserving Location-Based Services

Ren, Y., Li, X., Miao, Y., Deng, R. H., Weng, J., Ma, S., & Ma, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3141398 · Cited by 32

Upscaling aboveground biomass of larch (Larix olgensis Henry) plantations from field to satellite measurements: a comparison of individual tree-based and area-based approaches

Zhen, Z., Yang, L. Y., Ma, Y., Wei, Q., Jin, H. I., & Zhao, Y. (2022) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2055381 · Cited by 32

Supervised clustering for automated document classification and prioritization: a case study using toxicological abstracts

Varghese, A., Cawley, M. A., & Hong, T. (2017) · Environment Systems & Decisions · doi:10.1007/s10669-017-9670-5 · Cited by 33

Stock trend prediction using news articles : a text mining approach

Falinouss, P. (2007) · DiVA (Luleå University of Technology) · Cited by 31

A Data-Driven Approach for Battery System Safety Risk Evaluation Based on Real-World Electric Vehicle Operating Data

Jia, Z., Wang, Z., Sun, Z., Liu, P., Zhu, X., & Sun, F. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3324450 · Cited by 31

Detection of sickle cell disease using deep neural networks and explainable artificial intelligence

Goswami, N. G., Goswami, A., Sampathila, N., Bairy, G. M., Chadaga, K., & Belurkar, S. (2024) · Journal of Intelligent Systems · doi:10.1515/jisys-2023-0179 · Cited by 34

Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences

Hoppe, H., Dietrich, P., Marzahn, P., Weiß, T., Nitzsche, C., von Lukas, U. F., Wengerek, T., & Borg, E. (2024) · Remote Sensing · doi:10.3390/rs16091493 · Cited by 36

Analyzing emotion words to predict severity of software bugs

Yang, G., Baek, S., Lee, J.-W., & Lee, B. (2017) · ACM Symposium on Applied Computing · doi:10.1145/3019612.3019788 · Cited by 32

An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds

Xu, S., Wang, R., Wang, H., & Zheng, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2912455 · Cited by 31

Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)

Erkuş, E. C., & Purutçuoğlu, V. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.01.014 · Cited by 32

Detecting Cyberbullying Across Multiple Social Media Platforms Using Deep Learning

Mahat, M. (2021) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite51222.2021.9404736 · Cited by 37

IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus

Yin, R., Thwin, N. N., Zhuang, P., Zhang, Y., Lin, Z., & Kwoh, C. K. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3108971 · Cited by 31

Phenotypic Traits Extraction and Genetic Characteristics Assessment of Eucalyptus Trials Based on UAV-Borne LiDAR and RGB Images

Liao, L., Cao, L., Xie, Y., Luo, J., & Wang, G. (2022) · Remote Sensing · doi:10.3390/rs14030765 · Cited by 31

A framework for a forest ecological base map – An example from Norway

Ørka, H. O., Jutras-Perreault, M.-C., Næsset, E., & Gobakken, T. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108636 · Cited by 31

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)