A5102908540- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Imbalanced Data Classification Techniques
- Data Stream Mining Techniques
- Anomaly Detection Techniques and Applications
This paper presents a benchmark of supervised Automated Machine Learning (AutoML) tools. Firstly, we analyze the characteristics eight recent open-source AutoML tools (Auto-Keras, Auto-PyTorch, Auto-Sklearn, AutoGluon, H2O AutoML, rminer, TPOT and TransmogrifAI) describe twelve popular OpenML datasets that were used in (divided into regression, binary multi-class classification tasks). Then, perform comparison study with hundreds computational experiments based on three scenarios: General...
Automation and scalability are currently two of the main challenges Machine Learning (ML).This paper proposes an automated distributed ML framework that automatically trains a supervised learning model produces predictions independently dataset with minimum human input.The was designed for domain telecommunications risk management, which often requires models need to be quickly updated by non-ML-experts trained on vast amounts data.Thus, architecture assumes environment, in order deal big...