MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines

H Jalalian, RMO Cruz - 2024 International Joint Conference on …, 2024‏ - ieeexplore.ieee.org
Dynamic Selection (DS), where base classifiers are chosen from a classifier's pool for each
new instance at test time, has shown to be highly effective in pattern recognition. However …

Explicable recommendation model based on a time‐assisted knowledge graph and many‐objective optimization algorithm

R Zheng, L Wu, X Cai, Y Xu - Concurrency and Computation …, 2024‏ - Wiley Online Library
Existing research on recommender systems primarily focuses on improving a single
objective, such as prediction accuracy, often ignoring other crucial aspects of …

Sales Forecasting for New Products Using Homogeneity-Based Clustering and Ensemble Method

S Hwang, Y Lee, BK Jeon, SH Oh - Electronics, 2025‏ - search.proquest.com
Accurate sales forecasting for new products is critical in industries characterized by intense
competition, rapid innovation, and short product life cycles, such as the smartphone market …

Mtl-nfw: A meta-learning framework for automated noise filter selection and hyperparameter optimization in auto-ml

I Khan, X Zhang, R Kumar, SM Alhashmi, R Ali - 2024‏ - researchsquare.com
The extensive implementation of machine learning (ML) has transformed data analysis and
decision-making processes. However, the process of choosing suitable ML algorithms for a …

Meta-Learning for Automated Algorithm Selection in Healthcare: Enhancing Clinical Outcomes through Data-Driven Recommendation

I Khan, X Zhang, R Ali, M Habib - … International Conference on …, 2024‏ - ieeexplore.ieee.org
The integration of machine learning (ML) algorithms in healthcare is transforming
diagnostics, treatment planning, and patient management. However, the complexity and …