Meta-data Augmentation Based Search Strategy Through Generative Adversarial Network for AutoML Model Selection

Abstract

Automated machine learning (AutoML) attempts to automatically build appropriate learning model for given dataset. Despite the recent progress of meta-learning to find good instantiations for AutoML framework, it is still difficult and time-consuming to collect sufficient meta-data with high quality. Therefore, we propose a novel method named Meta-data Augmentation based Search Strategy (MDASS) for AutoML model selection, which is mainly composed of Meta-GAN Surrogate model (MetaGAN) and Self-Adaptive Meta-model (SAM). MetaGAN employs Generative Adversarial Network as surrogate model to collect effective meta-data based on the limited meta-data, which can alleviate the dilemma of meta-overfitting in meta-learning. Based on augmented meta-data, SAM self-adaptively builds multi-objective meta-model, which can select the algorithms with proper trade-off between learning performance and computational budget. Furthermore, for new datasets, MDASS combines promising algorithms and hyperparameter optimization to perform automated model selection under time constraint. Finally, the experiments on various classification datasets from OpenML and algorithms from scikit-learn are conducted. The results show that GAN is promising to incorporate with AutoML and MDASS can perform better than the competing approaches with time budget.