A Preliminary Study on Evolutionary Clustering for Multiple Instance Learning
Aurora Esteban, Amelia Zafra, Sebastián Ventura · 2020
Since its beginnings, multiple instance learning studies have shown an excellent performance in the areas where it has been applied. This efficiency is due to multiple instance learning allows to represent a complex object by a set of feature vectors, being a more flexible representation to preserve more information than one based on single feature vector. This paper attempts to progress in this area carrying out a first study that introduces evolutionary algorithms for solving multiple instance cluster analysis. Specifically, we present four proposals of genetic algorithms for multi-instance partitional clustering: three of them are adaptations of existing algorithms for single-instance clustering, while the last one is a novel approach based on CHC evolutionary algorithm. Moreover, two classic non-genetic partitional algorithms are included in the final comparison. Experimental results considering ten representative datasets show promising results for our proposal.