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MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines
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 …
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 …
objective, such as prediction accuracy, often ignoring other crucial aspects of …
Sales Forecasting for New Products Using Homogeneity-Based Clustering and Ensemble Method
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 …
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 …
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
The integration of machine learning (ML) algorithms in healthcare is transforming
diagnostics, treatment planning, and patient management. However, the complexity and …
diagnostics, treatment planning, and patient management. However, the complexity and …