Ristretto is a Java package intended to solve feature selection problems. It is based on ECJ and Java-ML.
It provides an individual representation based on subsets of selected features and specific mutation and crossover operators for this representation. It also provides NSGA-2-based multi-objective algorithms for supervised and unsupervised problems and a lexicographic co-evolutionary many-objective algorithm for supervised problems, able to optimize simultaneously the parameters of a classifier while the most relevant features are also being selected.
Ristretto requires Java SE 7 or above. It also depends on the following Java libraries:
- ECJ (version 24),
- Java-ML,
- Apache Commons Math, and
- LIBSVM, if SVM classifiers are desired
Some tests also make use of gnuplot to show their results graphically and MATLAB to make some statistics.
Ristretto is fully documented in its github-pages. You can also generate its docs from the source code. Simply change directory to the docs subfolder and type in make.
The tests subfolder contains several examples that show the basic usage of Ristretto.
- J. González, J. Ortega, M. Damas, P. Martín-Smith, J. Q. Gan. A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI, Neurocomputing, 333:407-418, 2019. https://doi.org/10.1016/j.neucom.2019.01.017
- J. González, J. Ortega, M. Damas, P. Martín-Smith. Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach. In: I. Rojas, G. Joya, A. Catala (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_39
- J. González, J. Ortega, J. J. Escobar, M. Damas. A Lexicographic Cooperative Co-Evolutionary Approach for Feature Selection, Neurocomputing, 463:59-76, 2021. https://doi.org/10.1016/j.neucom.2021.08.003
This work was supported by project Energy-aware High Performance Multi-objective Optimization in Heterogeneous Computer Architectures. Applications on Biomedical Engineering (e-hpMOBE), with reference TIN2015-67020-P, funded by the Spanish Ministerio de Economía y Competitividad, and by the European Regional Development Fund (ERDF).
Ristretto © 2015 EFFICOMP.