Autostacker: A Compositional Evolutionary Learning System
This addresses the need for efficient and accessible AutoML tools for users without machine learning expertise, though it is incremental as it builds on existing evolutionary and stacking methods.
The paper tackled the problem of automating machine learning pipeline design by introducing Autostacker, which uses a hierarchical stacking architecture and evolutionary algorithm to efficiently search for high-accuracy pipelines without requiring domain knowledge or preprocessing, achieving state-of-the-art or competitive performance on fifteen datasets in terms of test accuracy and time cost.
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.