Data mining: concepts and techniques

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138

Emergence of Scaling in Random Networks

Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Induction of decision trees

Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

The anatomy of a large-scale hypertextual Web search engine

Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004

Identification of ischemic heart disease via machine learning analysis on magnetocardiograms

Tantimongcolwat, T., Naenna, T., Isarankura-Na-Ayudhya, C., Embrechts, M. J., & Prachayasittikul, V. (2008) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2008.04.009 · Cited by 57

Multimodal mild depression recognition based on EEG-EM synchronization acquisition network

Zhu, J., Wang, Y., La, R., Zhan, J., Niu, J., Zeng, S., & Hu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2901950 · Cited by 56

Local Outlier Detection with Interpretation

Dang, X. H., Micenková, B., Assent, I., & Ng, R. T. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40994-3_20 · Cited by 56

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

Kaundinya, D. P., Balachandra, P., Ravindranath, N. H., & Ashok, V. (2013) · Energy · doi:10.1016/j.energy.2013.02.011 · Cited by 56

Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease

Nasrolahzadeh, M., Mohammadpoory, Z., & Haddadnia, J. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9499-8 · Cited by 51

Application of knowledge discovery in database (KDD) techniques in cost overrun of construction projects

Ghazal, M. M., & Hammad, A. (2022) · International Journal of Construction Management · doi:10.1080/15623599.2020.1738205 · Cited by 52

Detecting Fake News with Tweets’ Properties

Nyow, N. X., & Chua, H. N. (2019) · 2019 IEEE Conference on Application, Information and Network Security (AINS) · doi:10.1109/ains47559.2019.8968706 · Cited by 51

Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective

Tamaddoni, A., Stakhovych, S., & Ewing, M. (2016) · Journal of Service Research · doi:10.1177/1094670515616376 · Cited by 50

Rumors detection in Chinese via crowd responses

Cai, G., Wu, H., & Lv, R. (2014) · 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) · doi:10.1109/asonam.2014.6921694 · Cited by 47

Violent political rhetoric on Twitter

Kim, T. (2023) · Political Science Research and Methods · doi:10.1017/psrm.2022.12 · Cited by 43

Towards the design of real-time autonomous IoT NIDS

Alhowaide, A., Alsmadi, I., & Tang, J. (2023) · Cluster Computing · doi:10.1007/s10586-021-03231-5 · Cited by 43

Mining multimodal sequential patterns: a case study on affect detection

Martínez, H. P., & Yannakakis, G. N. (2011) · Proceedings of the 13th international conference on multimodal interfaces · doi:10.1145/2070481.2070485 · Cited by 42

SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset

Islam, S., Sara, U., Kawsar, A., Rahman, A., Kundu, D., Das, D., Rezaul, A. N. M., & Hasan, M. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120351 · Cited by 37

How to Cope with Change? - Preserving Validity of Predictive Services over Time

Baier, L., Kühl, N., & Satzger, G. (2019) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2019.133 · Cited by 33

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker, I. H. (2021) · doi:10.20944/preprints202102.0340.v1 · Cited by 295

A multiparadigm intelligent tutoring system for robotic arm training

Fournier-Viger, P., Nkambou, R., Nguifo, E. M., Mayers, A., & Faghihi, U. (2013) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2013.27 · Cited by 30

Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition

Department of Computer Science, A. I. U.-B., Sultan, T., Jahan, N., Basak, R., Jony, M. S. A., & Nabil, R. H. (2023) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2023.02.01 · Cited by 27

Developing Screening Services for Colorectal Cancer on Android Smartphones

Wu, H.-C., Chang, C.-J., Lin, C.-C., Tsai, M.-C., Chang, C.-C., & Tseng, M.-H. (2014) · Telemedicine and e-Health · doi:10.1089/tmj.2013.0288 · Cited by 23

Automating Tiny ML Intelligent Sensors DevOPS Using Microsoft Azure

Vuppalapati, C., Ilapakurti, A., Chillara, K., Kedari, S., & Mamidi, V. (2020) · 2020 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata50022.2020.9377755 · Cited by 24

An Artificial Neural Network Based Pattern Classification Algorithm for Diagnosis of Heart Disease

Tarle, B., & Jena, S. (2017) · 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) · doi:10.1109/iccubea.2017.8463729 · Cited by 20

A COMPARISON BETWEEN DIFFERENT CLASSIFIERS FOR TENNIS MATCH RESULT PREDICTION

Ghosh, S., Sadhu, S., Biswas, S., Sarkar, D., & Sarkar, P. P. (2019) · Malaysian Journal of Computer Science · doi:10.22452/mjcs.vol32no2.2 · Cited by 20

Predicting post-synaptic activity in proteins with data mining

Pappa, G. L., Baines, A. J., & Freitas, A. A. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1102 · Cited by 20

Text mining in long-term care: exploring the usefulness of artificial intelligence in a nursing home setting

Hacking, C., Verbeek, H., Hamers, J. P. H., Sion, K., & Aarts, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268281 · Cited by 19

MODELLING STUDENT INTERACTIONS IN INTELLIGENT LEARNING ENVIRONMENTS: CONSTRUCTING BAYESIAN NETWORKS FROM DATA

MAVRIKIS, M. (2010) · International Journal on Artificial Intelligence Tools · doi:10.1142/s0218213010000406 · Cited by 17

A Framework for Outlier Description Using Constraint Programming

Kuo, C.-T., & Davidson, I. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10174 · Cited by 18

Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait

Fernandes, C., Fonseca, L., Ferreira, F., Gago, M., Costa, L., Sousa, N., Ferreira, C., Gama, J., Erlhagen, W., & Bicho, E. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621466 · Cited by 17

Adaptive neuro-fuzzy inference system for customizing investment type based on the potential investors’ demographics and feedback

Asemi, A., Asemi, A., & Ko, A. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00784-7 · Cited by 13

Automated segmentation of insect anatomy from micro‐CT images using deep learning

Toulkeridou, E., Gutierrez, C. E., Baum, D., Doya, K., & Economo, E. P. (2023) · Natural Sciences · doi:10.1002/ntls.20230010 · Cited by 13

Exploring the relationship between regenerative grazing and Ranchers’ wellbeing

Vivas, J., & Hodbod, J. (2024) · Journal of Rural Studies · doi:10.1016/j.jrurstud.2024.103267 · Cited by 11

Automated invoice processing: Machine learning-based information extraction for long tail suppliers

Krieger, F., Drews, P., & Funk, B. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200285 · Cited by 11

Investigating the potential of NLP-driven linguistic and acoustic features for predicting human scores of children’s oral language proficiency

Hunte, M. R., McCormick, S., Shah, M., Lau, C., & Jang, E. E. (2021) · Assessment in Education: Principles, Policy & Practice · doi:10.1080/0969594x.2021.1999209 · Cited by 10

Spatiotemporal analysis of prehistoric settlement clustering based on terrain constraints in the Linfen area of China

Liu, F., Yu, L., Nie, Y., & Zhu, J. (2019) · Geoarchaeology · doi:10.1002/gea.21732 · Cited by 9

Manufacturing data-driven process adaptive design method

Wei, W., Yuan, J., & Liu, A. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.02.230 · Cited by 8

The great methods bake-off: Comparing performance of machine learning algorithms

Kigerl, A., Hamilton, Z., Kowalski, M., & Mei, X. (2022) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2022.101946 · Cited by 8

An Artificial Neural Network Approach for Classifying Cricket Batsman's Performance by Adam Optimizer and Prediction by Derived Attributes

Mody, K., Malathi, D., & Jayaseeli, J. D. D. (2021) · 2021 Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr51658.2021.9588836 · Cited by 6

Untitled work

(2010) · doi:10.1007/978-1-4614-0373-9_31 · Cited by 3