arXiv:2101.05993v2 Announce Type: replace-cross Abstract: Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning. Existing algorithm recommendation models are typically trained with individual learners and rely on only one type of meta-feature, which may limit their ability to capture the diverse characteristics of classification problems. This paper proposes a multi-view ensemble meta-learning framework for classification algorithm recommendation. The framework constructs base recommendation models from differen
The continuous growth in machine learning applications necessitates more efficient and accurate algorithm selection, pushing research towards meta-learning and ensemble approaches.
Improved classification algorithm recommendation can significantly enhance the efficiency and performance of AI solutions across various industries, accelerating practical applications and development cycles.
The ability to more reliably select optimal classification algorithms will reduce trial-and-error in machine learning deployments, potentially leading to faster model development and better outcomes.
- · Machine Learning Engineers
- · Data Scientists
- · AI-driven Software Companies
- · Manual algorithm selection processes
More robust and efficient deployment of machine learning models in real-world applications.
Increased demand for tools and platforms integrating advanced meta-learning recommendation systems.
Democratization of advanced machine learning capabilities, allowing non-experts to build high-performing AI systems more easily.
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