An introduction to variable and feature selection

Feature selection and classification of imbalanced datasets

Duchesnay, E., Cachia, A., Boddaert, N., Chabane, N., Mangin, J.-F., Martinot, J.-L., Brunelle, F., & Zilbovicius, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.011 · Cited by 60

Determining the Veracity of Rumours on Twitter

Giasemidis, G., Singleton, C., Agrafiotis, I., Nurse, J. R. C., Pilgrim, A., Willis, C., & Greetham, D. V. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-47880-7_12 · Cited by 60

How to Address the Data Quality Issues in Regression Models: A Guided Process for Data Cleaning

Corrales, D., Corrales, J., & Ledezma, A. (2018) · Symmetry · doi:10.3390/sym10040099 · Cited by 58

Machine Learning Approach to Simulate Soil CO2 Fluxes under Cropping Systems

Adjuik, T. A., & Davis, S. C. (2022) · Agronomy · doi:10.3390/agronomy12010197 · Cited by 56

Comparison of machine learning models to provide preliminary forecasts of real estate prices

Chou, J.-S., Fleshman, D.-B., & Truong, D.-N. (2022) · Journal of Housing and the Built Environment · doi:10.1007/s10901-022-09937-1 · Cited by 54

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · doi:10.20944/preprints201803.0147.v1 · Cited by 143

Predicting clinical responses in major depression using intrinsic functional connectivity

Qin, J., Shen, H., Zeng, L.-L., Jiang, W., Liu, L., & Hu, D. (2015) · NeuroReport · doi:10.1097/wnr.0000000000000407 · Cited by 43

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Jha, A., Quesnel-Vallières, M., Wang, D., Thomas-Tikhonenko, A., Lynch, K. W., & Barash, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02681-3 · Cited by 39

A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints

Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., & Reuber, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217388 · Cited by 37

Feature Augmentation with Reinforcement Learning

Liu, J., Chai, C., Luo, Y., Lou, Y., Feng, J., & Tang, N. (2022) · 2022 IEEE 38th International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00317 · Cited by 33

Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

R.G., G., Sajjanhar, A., & Xiang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123533 · Cited by 28

Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features

Han, X., Pang, J., Xu, D., Wang, R., Xie, F., Yang, Y., Sun, J., Li, Y., Li, R., Yin, X., et al. (2023) · Physiological Measurement · doi:10.1088/1361-6579/ad0f70 · Cited by 25

Incremental relevance sample-feature machine: a fast marginal likelihood maximization approach for joint feature selection and classification

Mohsenzadeh, Y., Sheikhzadeh, H., & Nazari, S. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.06.028 · Cited by 22

Stock Market Trend Prediction Using Support Vector Machines and Variable Selection Methods

Grigoryan, H. (2017) · Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017) · doi:10.2991/ammsa-17.2017.45 · Cited by 17

Self-Tuned Descriptive Document Clustering Using a Predictive Network

Brockmeier, A. J., Mu, T., Ananiadou, S., & Goulermas, J. Y. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2781721 · Cited by 15

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

Rao, W., Qian, X., Fan, Y., & Liu, T. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.161543 · Cited by 15

A novel somatic cancer gene-based biomedical document feature ranking and clustering model

Bikku, T., & Paturi, R. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100188 · Cited by 15

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets

Murtojärvi, M., Halkola, A. S., Airola, A., Laajala, T. D., Mirtti, T., Aittokallio, T., & Pahikkala, T. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.104014 · Cited by 14

Quantum computing and supervised machine learning

Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11

Investigating the determinants of performance in machine learning for protein fitness prediction

(2025) · doi:10.1002/pro.70235 · Cited by 6

A Feature Selection Method for Multimodal Multispectral LiDAR Sensing

Han, Y., Salido-Monzú, D., Butt, J. A., Schweizer, S., & Wieser, A. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.04.022 · Cited by 6

A new multiple kernel-based regularization method for identification of delay linear dynamic systems

Chen, X., Mao, Z., & Jia, R. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.103971 · Cited by 5

Parsimonious statistical learning models for low flow estimation

Laimighofer, J., Melcher, M., & Laaha, G. (2021) · doi:10.5194/hess-2021-481 · Cited by 30

Artificial intelligence in diabetes management

Thomas, A., Gopi, V. P., & Francis, B. (2024) · Advances in Artificial Intelligence · doi:10.1016/b978-0-443-19073-5.00002-1 · Cited by 3

Untitled work

(2017) · doi:10.9728/dcs.2024.25.7.1847 · Cited by 2