BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
This work addresses the problem of making machine learning more accessible and efficient for non-expert practitioners, though it is incremental as it builds on existing automated machine learning techniques.
The paper tackles the challenge of automating machine learning model selection and hyperparameter optimization for non-experts by proposing BOASF, a framework combining Bayesian optimization and adaptive successive filtering, which speeds up these processes and achieves better prediction performance than state-of-the-art methods, with improved anytime performance under various time budgets.
Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning task successfully and efficiently. Finding the optimal machine learning model or the hyperparameter combination set from a large number of possible alternatives usually requires considerable expert knowledge and experience. To tackle this problem, we propose a combined Bayesian Optimization and Adaptive Successive Filtering algorithm (BOASF) under a unified multi-armed bandit framework to automate the model selection or the hyperparameter optimization. Specifically, BOASF consists of multiple evaluation rounds in each of which we select promising configurations for each arm using the Bayesian optimization. Then, ASF can early discard the poor-performed arms adaptively using a Gaussian UCB-based probabilistic model. Furthermore, a Softmax model is employed to adaptively allocate available resources for each promising arm that advances to the next round. The arm with a higher probability of advancing will be allocated more resources. Experimental results show that BOASF is effective for speeding up the model selection and hyperparameter optimization processes while achieving robust and better prediction performance than the existing state-of-the-art automatic machine learning methods. Moreover, BOASF achieves better anytime performance under various time budgets.