Our paper “Analysis of Differential Evolution variants for parameter tuning of Decision Trees Inductive Algorithms” was accepted for its presentation at NEO 2019, to be held in Saltillo, México. The full program of the event will be available on August, 20th. Congratulations to the co-authors David Martínez Galicia y Efrén Mezura Montes.
Extended abstract: This paper presents an empirical comparison of some Differential Evolution variants to solve the parameter tuning of Decision Tree induction algorithms. The aim of this analysis is to identify which one of the variants is more competitive to this problem. In this work, an EVolutionary Agents \& Artifacts approach for the induction of Decision Trees (eva2dt), is adopted to maximize the classification accuracy.
The Agents & Artifacts paradigm naturally accommodates the concept of an evolving population of agents inducing and evaluating decision trees, using different tools implemented as Weka-based artifacts. The use of different inductive algorithms, instead of a single one, is a novelty with respect to related work. Eva2dt agents represent potential solutions for the Differential Evolution algorithm, which are mapped to parameters for inductive algorithms, e.g., j48, SimpleCART, RepTree and RandomTree. Each agent is able to induce decisions trees and to also compute their accuracy through a 10-folds cross-validation process.
The assessed Differential Evolution variants vary in the recombination operator adopted and in some self-adaptive mechanisms. A set of statistical tests were performed to validate the obtained results. All variants were tested on 16 public datasets, obtaining competitive results, particularly for the datasets with the fewer number of training instances. Preliminary results suggest that DE/rand/1/bin variant outperform the others, even when more generations are added. These observations will help to develop other mechanisms to improve the current performance (convergence and accuracy).