TL;DR
The proposed iLEAD method combines hybrid genetic programming with a CART classifier, offering improved predictive performance, reduced processing time, and lower memory usage compared to traditional GP-based decision trees, making it ideal for incremental learning in dynamic datasets.
This chapter analyzes two machine learning-based approaches to automated concept drift detection-a novel approach based on One-Class Support Vector Machines (OCSVM) and a previously-studied technique based on Minibatch K-Means (MK-Means), which achieve classification accuracy comparable to periodic retraining, while of...
The proposed meta-adaptive resampling selection plus plus (MARS) is a stability aware meta-adaptive framework that chooses the best method based on the data based on the data, suggesting that selecting methods based on stability worked well on the evaluated datasets.
The MLIDSC introduces an automated labeling mechanism that eliminates the need for prior parameter assumptions and uses a novel weighted scheme that combines the imbalance ratio and the importance of individual instances at a given time, ensuring a focus on critical data points.
This paper presents an Evolutionary Meta-Learning Framework (EMLF) for dynamic feature selection in high-velocity, non-stationary data streams. Unlike static methods, EMLF continuously updates the selected feature subset as feature relevance changes over time. A multi-objective fitness function, normalized using predic...
This research presents an Automated Machine Learning (AutoML) data pipeline designed to enhance machine learning workflows for diverse data types, including structured tabular (CSV) and time-series datasets. The proposed system automates key processes such as data preprocessing, model selection, and architecture optimi...
ASWBoost, a robust boosting algorithm, is proposed by introducing a parameter θ to modify the exponential loss, and significantly outperforms AdaBoost, CS-AdaBoost, GBDT, SMOTEBoost, and RUSBoost, with overall performance comparable to XGBoost.
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