While machine learning has evolved at a fast pace in the last decades, the testing procedure of new methods may be not keeping pace. It often relies on well-studied collections of classification datasets such as the UCI repository. However, a meta-Analysis through features has showed that most datasets from UCI are not suffciently challenging to expose unique weaknesses of algorithms. In this paper we present a method to generate datasets with continuous, binary and categorical attributes, through the fitting of a Gaussian Mixture Model and a set of generalized Bernoulli distributions. By targeting empty areas of the instance space, this method has the potential to generate datasets with more diverse feature values.i.
|Title of host publication||Proceedings of the Genetic and Evolutionary Computation Conference Companion|
|Subtitle of host publication||2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017; Berlin; Germany; 15 July 2017 through 19 July 2017; Code 128763|
|Place of Publication||New York NY USA|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||7|
|Publication status||Published - 15 Jul 2017|
|Event||The Genetic and Evolutionary Computation Conference 2017 - Berlin, Germany|
Duration: 15 Jul 2017 → 19 Jul 2017
|Conference||The Genetic and Evolutionary Computation Conference 2017|
|Abbreviated title||GECCO 2017|
|Period||15/07/17 → 19/07/17|
|Other||A Recombination of the 26th International Conference on Genetic Algorithms (ICGA) and the 22nd Annual Genetic Programming Conference (GP).|
The Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms,genetic programming, ant colony optimization and swarm intelligence, complex systems (artificiallife/robotics/evolvable hardware/generative and developmental systems/artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more.