AI Strategy and Concepts Bibliography

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Chen, B., Wu, H., Mo, W., Chattopadhyay, I., & Lipson, H. 2018, Autostacker: A compositional evolutionary learning system. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference. 
Added by: SijanLibrarian (2020-11-26 02:22:49)   Last edited by: SijanLibrarian (2020-11-26 02:24:50)
Resource type: Proceedings Article
BibTeX citation key: Chen2018a
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Categories: Artificial Intelligence, Computer Science, Data Sciences, Decision Theory, General, Innovation, Mathematics
Subcategories: Big data, Decision making, Deep learning, Edge AI, Machine learning, Machine recognition, Markov models, Q-learning
Creators: Chattopadhyay, Chen, Lipson, Mo, Wu
Collection: Proceedings of the Genetic and Evolutionary Computation Conference
Views: 52/71
Views index: 20%
Popularity index: 5%
Abstract
In this work, an automatic machine learning (AutoML) modeling architecture called Autostacker is introduced. Autostacker combines an innovative hierarchical stacking architecture and an evolutionary algorithm (EA) to perform efficient parameter search without the need for prior domain knowledge about the data or feature preprocessing. Using EA, Autostacker quickly evolves candidate pipelines with high predictive accuracy. These pipelines can be used in their given form, or serve as a starting point for further augmentation and refinement by human experts. Autostacker finds innovative machine learning model combinations and structures, rather than selecting a single model and optimizing its hyperparameters. When its performance on fifteen datasets is compared with that of other AutoML systems, Autostacker produces superior or competitive results in terms of both test accuracy and time cost.
  
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