Towards a method for automatically selecting and configuring multi-label classification algorithms

Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automatic ML (Auto-ML) area has emerged to solve this task. With this issue in mind, in this work we are interested in a specific type of classification problem, called multi-label classification (MLC). In MLC, each example in the dataset can be associated to one or more class labels, making the task considerably harder than traditional, single-label classification. In addition, the cost of learning raises due to the higher complexity of the data. Although the literature has proposed some methods to solve the Auto-ML task, those methods address only the traditional, single-label classification problem. By contrast, this work proposes the first method (an evolutionary algorithm) for solving the Auto-ML task in MLC, i.e., the first method for automatically selecting and configuring the best MLC algorithm for a given input dataset. The proposed evolutionary algorithm is evaluated on three MLC datasets, and compared against two baseline methods according to four different multi-label predictive accuracy measures. The results show that the proposed evolutionary algorithm is competitive against the baselines, but there is still room for improvement.