Autostacker: A Compositional Evolutionary Learning System
arXiv:1803.00684
Abstract
We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search. Neither prior domain knowledge about the data nor feature preprocessing is needed. Using EA, Autostacker quickly evolves candidate pipelines with high predictive accuracy. These pipelines can be used as is or as a starting point for human experts to build on. Autostacker finds innovative combinations and structures of machine learning models, rather than selecting a single model and optimizing its hyperparameters. Compared with other AutoML systems on fifteen datasets, Autostacker achieves state-of-art or competitive performance both in terms of test accuracy and time cost.
Submitted to GECCO 2018 and currently under review
References in corpus (7)
- Scikit-learn: Machine Learning in Python
- Popular Ensemble Methods: An Empirical Study
- OpenML: networked science in machine learning
- Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
- Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
- Automating biomedical data science through tree-based pipeline optimization
- PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison