A genetic algorithm for improving the induction of attribute-based decision graph classifiers
João Roberto Bertini, Maria do Carmo Nicoletti · 2016
Attribute-based Decision Graphs (AbDG) have been recently proposed as a novel and effective way to represent data as weighted labeled graphs. However, for some domains, the definition of a graph structure that best fits the data can be a hard task. In machine learning it is very common to rely on evolutionary algorithms to guide the model selection phase of learning processes. Particularly, as far as classification tasks are concerned, evolutionary algorithms can be of great help when searching for characteristics which would promote the induction of a suitable classifier, without the need to exhaustively test all possibilities. This paper proposes a genetic algorithm (GA-AbDG) which explores the possible benefits of inducing part of the structure of AbDGs, by evolving suitable edge sets for them. In addition, the paper shows that the results obtained with the GA-AbDG algorithm outperform a prior proposal, with a fixed p-partite structure of AbDGs, as well as the C4.5.