One-step learning algorithm selection for classification via convolutional neural networks
This work addresses the challenge of automating classifier selection for practitioners, offering a more efficient approach, though it is incremental as it builds on existing meta-learning concepts.
The paper tackles the problem of algorithm selection for binary classification by proposing a one-step method using convolutional neural networks trained directly on tabular data, eliminating the need for explicit meta-features; experiments show near-perfect performance on simulated datasets, outperforming traditional two-step methods.
As with any task, the process of building machine learning models can benefit from prior experience. Meta-learning for classifier selection leverages knowledge about the characteristics of different datasets and/or the past performance of machine learning techniques to inform better decisions in the current modeling process. Traditional meta-learning approaches first collect metadata that describe this prior experience and then use it as input for an algorithm selection model. In this paper, however, a one-step scheme is proposed in which convolutional neural networks are trained directly on tabular datasets for binary classification. The aim is to learn the underlying structure of the data without the need to explicitly identify meta-features. Experiments with simulated datasets show that the proposed approach achieves near-perfect performance in identifying both linear and nonlinear patterns, outperforming the conventional two-step method based on meta-features. The method is further applied to real-world datasets, providing recommendations on the most suitable classifiers based on the data's inherent structure.