Multi-label Classification Using Vector Generalized Additive Model via Cross-Validation

Abstract

Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or simultaneously.
However, most models cannot classify the target simultaneously, and this is not expected to happen in the modeling rule. This study
was conducted to propose a novel solution in the form of a Vector Generalized Additive Model Using Cross-Validation (VGAMCV) to
address these problems. The proposed method leverages the Vector Generalized Additive Model (VGAM), which is a semi-parametric
model combining both parametric and non-parametric components as the underlying base model. Cross-validation was also applied
to tune the parameters to optimize the performance of the method. Moreover, the methodology of VGAMCV was compared with a
tree-based model, Random Forest, commonly used in multi-label classification to evaluate its effectiveness based on fourteen metric
scores. The results showed positive outcomes as indicated by 0.703 average accuracy and 0.601 Area Under Curve (AUC) recorded, but
these improvements were not statistically significant. Meanwhile, the method offered a viable alternative for multi-label classification
tasks, and its introduction served as a contribution to the expanding repertoire of methods available for this purpose.