Outputs (2)

Feature Importance Identification for Time Series Classifiers (2022)
Presentation / Conference Contribution
Meng, H., Wagner, C., & Triguero, I. (2022, October). Feature Importance Identification for Time Series Classifiers. Presented at 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic

Time series classification is a challenging research area where machine learning techniques such as deep learning perform well, yet lack interpretability. Identifying the most important features for such classifiers provides a pathway to improving th... Read More about Feature Importance Identification for Time Series Classifiers.

Accelerated pattern search with variable solution size for simultaneous instance selection and generation (2022)
Presentation / Conference Contribution
Le, H. L., Neri, F., Landa-Silva, D., & Triguero, I. (2022, July). Accelerated pattern search with variable solution size for simultaneous instance selection and generation. Poster presented at Genetic and Evolutionary Computation Conference Companion (GECCO 2022), Boston, USA and online

The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is o... Read More about Accelerated pattern search with variable solution size for simultaneous instance selection and generation.