In view of this point, features that are critical to the load forecasting are selected using an embedded feature selection algorithm based on LightGBM to form an optimal feature set, with which a sequence to se
Based on the improved Salp Swarm Algorithm, a threshold voting-based feature selection framework for IoT traffic data has been designed to remove redundant and irrelevant features and reduce computational training time, space and time complexity. Combined with the LightGBM ensemble learning model, a li...
LightGBM utilizes a histogram-based algorithm11 that discretizes features through value binning, using bin medians as histogram indices for split point selection (Fig. 1). This approach reduces memory usage and computational costs while providing implicit regularization to enhance model robustness. Fig....
Rtayli and Enneya [14] applied a supervised feature selection method, Random Forest, to identify the most predictive features. Random Forest (RF) is an ensemble learning algorithm that is trained in parallel through bagging [15]. Recently, RF has been increasingly exploited as a feature selecti...
[18] is a supervised feature selection algorithm that is designed as a wrapper around a Random Forest classifier to identify important features in a dataset. They kept the features with an importance score of 0.5 or higher to train the Autoencoder for each iteration. The model detected credit ...
To address these issues, we propose an efficient intrusion detection model, which is based on hybrid feature selection and stack ensemble learning. Our hybrid feature selection method, called MI-Boruta, combines mutual information (MI) as a filter method and the Boruta algorithm as a wrapper ...
GrootCV will automatically detect imbalanced data and set the lightGBM'is_unbalance' = True For Leshy and BoostAGroota, you can pass the estimator with the relevant parameter (e.g.class_weight = 'balanced') Boruta The Boruta algorithm tries to capture all the important features you might have...
CatBoost, Light GBM, and XGBoost are gradient-boosted decision trees (GBDTs) [56], which are ensembles of sequentially trained trees. An ensemble classifier combines weak algorithms, or instances of an algorithm, into a strong learner. CatBoost relies on ordered boosting to order the instances us...
Ji, Y., Lu, C., Liu, L.et al.Advancing bankruptcy prediction: a study on an improved rime optimization algorithm and its application in feature selection.Int. J. Mach. Learn. & Cyber.(2025). https://doi.org/10.1007/s13042-024-02462-3 ...
The feature selection process is an important process in high-dimensional data mining applications. It involves selecting a subset of relevant features and applying them to the given learning algorithm. The benefits of using feature selection methods include data reduction and better visualization of the...