An effective action covering for multi-label learning classifier systems
Shabnam Nazmi, Abdollah Homaifar, Mohd M. Anwar · Proceedings of the Genetic and Evolutionary Computation Conference · 2021
In Multi-label (ML) classification, each instance is associated with more than one class label. Incorporating the label correlations into the model is one of the increasingly studied areas in the last decade, mostly due to its potential in training more accurate predictive models and dealing with noisy/missing labels. Previously, multi-label learning classifier systems have been proposed that incorporate the high-order label correlations into the model through the label powerset (LP) technique. However, such a strategy cannot take advantage of the valuable statistical information in the label space to make more accurate inferences. Such information becomes even more crucial in ML image classification problems where the number of labels can be very large. In this paper, we propose a multi-label learning classifier system that leverages a structured representation for the labels through undirected graphs to utilize the label similarities when evolving rules. More specifically, we propose a novel scheme for covering classifier actions, as well as a method to calculate ML prediction arrays. The effectiveness of this method is demonstrated by experimenting on multiple benchmark datasets and comparing the results with multiple well-known ML classification algorithms.