Data Set Used
This work presents a new evolutionary ensemble method for data classification, which is inspired by the concepts of <i>bagging</i> and <i>boosting</i>, and aims at combining their good features while avoiding their weaknesses. The approach is based on a distributed multiple-population genetic programming (GP) algorithm which exploits the technique of… (More)
In this paper we argue that the performance of evolutionary computation on sequential decision problems strongly depends on the characteristics of the task at hand. On "error-avoidance" tasks, in which the decision process is interrupted every time a bad decision is made, evolutionary methods usually perform well. However, the same is not true for… (More)
The present work treats the data classification task by means of evolutionary computation techniques using three ingredients: genetic programming, competitive coevolution, and context-free grammar. The robustness and symbolic/interpretative qualities of the genetic programming are employed to construct classification trees via Darwinian evolution. The… (More)
One issue in data classification problems is to find an optimal subset of instances to train a classifier. Training sets that represent well the characteristics of each class have better chances to build a successful predictor. There are cases where data are redundant or take large amounts of computing time in the learning process. To overcome this issue,… (More)
The field of instance selection (IS) concerns the determination of an optimal subset of data that has two fundamental properties: (i) the new obtained set is smaller than the original data set; and (ii) it retains the essential set of instances needed to build an accurate classifier. This work proposes a new IS approach based on the support vector machine… (More)
A real-world problem, namely to improve the recruitment effectiveness of a certain company, is tackled here by evolving accurate and human-readable classifiers by means of grammar-based genetic programming techniques.