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KEA

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Large-Scale Bayesian Logistic Regression for Text Categorization

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Functional Trees

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Unsupervised document classification using sequential information maximization

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Best-first Decision Tree Learning

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Stacking Bagged and Dagged Models

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Comparative Evaluation of Approaches to Propositionalization

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Combining Naive Bayes and Decision Tables

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Ensembles of nested dichotomies for multi-class problems

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Discriminative parameter learning for Bayesian networks

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Weka4WS: A WSRF-Enabled Weka Toolkit for Distributed Data Mining on Grids

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An Optimization Perspective on Kernel Partial Least Squares Regression

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Weightily Averaged One-Dependence Estimators

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Ensembles of Balanced Nested Dichotomies for Multi-class Problems

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GATE: an environment to support research and development in natural language engineering

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Efficient lazy elimination for averaged one-dependence estimators

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BioWeka—extending the Weka framework for bioinformatics

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Mayday-a microarray data analysis workbench

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Statistical Learning in Multiple Instance Problems

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Fiji: an open-source platform for biological-image analysis

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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

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A survey on concept drift adaptation

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735 · see citing works

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

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Explaining prediction models and individual predictions with feature contributions

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Survey of intrusion detection systems: Techniques, datasets and challenges

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A general-purpose machine learning framework for predicting properties of inorganic materials

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The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

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Machine-learning-assisted materials discovery using failed experiments

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Introducing a New Benchmarked Dataset for Activity Monitoring

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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

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Relief-based feature selection: Introduction and review

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Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research

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Machine learning with big data: challenges and approaches.

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An empirical comparison of botnet detection methods

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A survey on unsupervised outlier detection in high‐dimensional numerical data

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From DFT to machine learning: recent approaches to materials science–a review

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Machine Learning Testing: Survey, Landscapes and Horizons

Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897 · see citing works

Enhanced sampling techniques in molecular dynamics simulations of biological systems

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The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

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Multiple instance learning: A survey of problem characteristics and applications

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Harnessing context sensing to develop a mobile intervention for depression

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Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2012) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2012.75 · Cited by 634 · see citing works

The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021) · IEEE Access · doi:10.1109/access.2021.3060863 · Cited by 616 · see citing works

Detection and Resolution of Rumours in Social Media

Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 608 · see citing works

A Survey on Ensemble Learning for Data Stream Classification

Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579 · see citing works

Fusion of Smartphone Motion Sensors for Physical Activity Recognition

Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2014) · Sensors · doi:10.3390/s140610146 · Cited by 552 · see citing works

Classification of time series by shapelet transformation

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Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works

A Survey of Online Activity Recognition Using Mobile Phones

Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2015) · Sensors · doi:10.3390/s150102059 · Cited by 489 · see citing works

Fame for sale: Efficient detection of fake Twitter followers

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 461 · see citing works

MIFS-ND: A mutual information-based feature selection method

Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.04.019 · Cited by 442 · see citing works

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

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Primary sclerosing cholangitis is characterised by intestinal dysbiosis independent from IBD

Sabino, J., Vieira-Silva, S., Machiels, K., Joossens, M., Falony, G., Ballet, V., Ferrante, M., Van Assche, G., Van der Merwe, S., Vermeire, S., et al. (2016) · Gut · doi:10.1136/gutjnl-2015-311004 · Cited by 413 · see citing works

Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies

Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0388-3 · Cited by 434 · see citing works

Measuring Syntactic Complexity in L2 Writing Using Fine‐Grained Clausal and Phrasal Indices

KYLE, K., & CROSSLEY, S. A. (2018) · The Modern Language Journal · doi:10.1111/modl.12468 · Cited by 405 · see citing works

Too much, too little, or just right? Ways explanations impact end users' mental models

Kulesza, T., Stumpf, S., Burnett, M., Yang, S., Kwan, I., & Wong, W.-K. (2013) · 2013 IEEE Symposium on Visual Languages and Human Centric Computing · doi:10.1109/vlhcc.2013.6645235 · Cited by 388 · see citing works

Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement

Forman, G., & Scholz, M. (2010) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1882471.1882479 · Cited by 368 · see citing works

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365 · see citing works

An intelligent healthcare monitoring framework using wearable sensors and social networking data

Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K.-S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.047 · Cited by 355 · see citing works

A Review of User Interface Design for Interactive Machine Learning

Dudley, J. J., & Kristensson, P. O. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3185517 · Cited by 343 · see citing works

Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles

Lines, J., Taylor, S., & Bagnall, A. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3182382 · Cited by 333 · see citing works

Human pluripotent stem cell-derived neural constructs for predicting neural toxicity

Schwartz, M. P., Hou, Z., Propson, N. E., Zhang, J., Engstrom, C. J., Costa, V. S., Jiang, P., Nguyen, B. K., Bolin, J. M., Daly, W., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516645112 · Cited by 325 · see citing works

Design, implementation and validation of a novel open framework for agile development of mobile health applications

Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado-Terriza, J. A., Lee, S., Pomares, H., & Rojas, I. (2015) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-14-s2-s6 · Cited by 321 · see citing works

Classification Accuracies of Physical Activities Using Smartphone Motion Sensors

Wu, W., Dasgupta, S., Ramirez, E. E., Peterson, C., & Norman, G. J. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2208 · Cited by 316 · see citing works

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease

Smedley, D., Schubach, M., Jacobsen, J. O. B., Köhler, S., Zemojtel, T., Spielmann, M., Jäger, M., Hochheiser, H., Washington, N. L., McMurry, J. A., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.07.005 · Cited by 283 · see citing works

Chemokines in depression in health and in inflammatory illness: a systematic review and meta-analysis

Leighton, S. P., Nerurkar, L., Krishnadas, R., Johnman, C., Graham, G. J., & Cavanagh, J. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.205 · Cited by 291 · see citing works

Creating and benchmarking a new dataset for physical activity monitoring

Reiss, A., & Stricker, D. (2012) · Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments · doi:10.1145/2413097.2413148 · Cited by 295 · see citing works

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.001 · Cited by 288 · see citing works

Real-time sleep apnea detection by classifier combination

Xie, B., & Minn, H. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2188299 · Cited by 294 · see citing works

Continuous Authentication for Voice Assistants

Feng, H., Fawaz, K., & Shin, K. G. (2017) · Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking · doi:10.1145/3117811.3117823 · Cited by 293 · see citing works

Activity-Aware Mental Stress Detection Using Physiological Sensors

Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., & Griss, M. (2012) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-29336-8_12 · Cited by 292 · see citing works

Multimodal Deep Learning for Activity and Context Recognition

Radu, V., Tong, C., Bhattacharya, S., Lane, N. D., Mascolo, C., Marina, M. K., & Kawsar, F. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3161174 · Cited by 281 · see citing works

Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis

Mota, N. B., Vasconcelos, N. A. P., Lemos, N., Pieretti, A. C., Kinouchi, O., Cecchi, G. A., Copelli, M., & Ribeiro, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034928 · Cited by 265 · see citing works

PhishStorm: Detecting Phishing With Streaming Analytics

Marchal, S., Francois, J., State, R., & Engel, T. (2014) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2014.2377295 · Cited by 271 · see citing works

Unobtrusive Sleep Monitoring using Smartphones

Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., Li, T., Chen, Y., Choudhury, T., & Cambell, A. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/icst.pervasivehealth.2013.252148 · Cited by 300 · see citing works

Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models

Flesia, L., Monaro, M., Mazza, C., Fietta, V., Colicino, E., Segatto, B., & Roma, P. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9103350 · Cited by 291 · see citing works

Using psycho-physiological measures to assess task difficulty in software development

Fritz, T., Begel, A., Müller, S. C., Yigit-Elliott, S., & Züger, M. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568266 · Cited by 262 · see citing works

Discrete Bayesian Network Classifiers: A Survey

Bielza, C., & Larrañaga, P. (2014) · ACM Computing Surveys · doi:10.1145/2576868 · Cited by 248 · see citing works

The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits

Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., & Rigoll, G. (2014) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.02.006 · Cited by 246 · see citing works

Automated classification of primary progressive aphasia subtypes from narrative speech transcripts

Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229 · see citing works

DCU: Aspect-based Polarity Classification for SemEval Task 4

Wagner, J., Arora, P., Cortes, S., Barman, U., Bogdanova, D., Foster, J., & Tounsi, L. (2014) · Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) · doi:10.3115/v1/s14-2036 · Cited by 222 · see citing works

Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors

Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2169804 · Cited by 221 · see citing works

Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors

Majid Mehmood, R., Du, R., & Lee, H. J. (2017) · IEEE Access · doi:10.1109/access.2017.2724555 · Cited by 213 · see citing works

The INTERSPEECH 2012 speaker trait challenge

Schuller, B., Steidl, S., Batliner, A., Nöth, E., Vinciarelli, A., Burkhardt, F., Son, R. V., Weninger, F., Eyben, F., Bocklet, T., et al. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-86 · Cited by 211 · see citing works

Towards reproducible computational drug discovery

Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0408-x · Cited by 208 · see citing works

Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach

Awad, A., Bader-El-Den, M., McNicholas, J., & Briggs, J. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.10.002 · Cited by 208 · see citing works

Online state of health estimation on NMC cells based on predictive analytics

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Identifying mild cognitive impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features

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Deep Learning Role in Early Diagnosis of Prostate Cancer

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