A new approach to ALMMo-0 Classifiers: A trade-off between accuracy and complexity

Abstract

In this paper, a new approach to the usage of 0-order Autonomous Learning Multi-Model (ALMMo-0) classifiers is proposed. ALMMo-0 classifiers are fully automatic and do not rely on any hyper-parameters. The creation of clouds relies on normalizing data points by their norm, which may remove an important degree of freedom from the data itself. The proposed approach consists of adding the initial radius of the clouds as an hyper-parameter, which makes it possible to skip the normalization step. This approach requires the search for the ideal value of the hyper-parameter. This way, upon training a set of models with different values for the initial radius, the user is expected to be able to choose from several models which range from more accurate to less complex. This approach was tested on three benchmark problems and compared to the results obtained using the original approach. Furthermore, this approach was also tested on a real dataset (Acute Kidney Injury). The obtained results enhance the versatility provided by the proposed method, successfully allowing the user to choose the model that fits better the design demands regarding accuracy, training time, and complexity.