Effective lazy learning algorithm based on a data gravitation model for multi-label learning

In the last decade, an increasing number of real-world problems surrounding multi-label data have appeared, and multi-label learning has become an important area of research. The data gravitation model is an approach that applies the principles of the universal law of gravitation to resolve machine learning problems. One advantage of the data gravitation model, compared with other techniques, is that it is based on simple principles with high performance levels. This paper presents a multi-label lazy algorithm based on a data gravitation model, named MLDGC. MLDGC directly handles multi-label data, and considers each instance as an atomic data particle. The proposed multi-label lazy algorithm was evaluated and compared to several state-of-the-art multi-label lazy methods on 34 datasets. The results showed that our proposal outperformed state-of-the-art lazy methods. The experimental results were validated using non-parametric statistical tests, confirming the effectiveness of this data gravitation model for multi-label lazy learning.