All Outputs (7)

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2021). EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Applied Soft Computing, 101, https://doi.org/10.1016/j.asoc.2020.107033

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Decomposition-Fusion for Label Distribution Learning (2020)
Journal Article
González, M., González-Almagro, G., Triguero, I., Cano, J., & García, S. (2021). Decomposition-Fusion for Label Distribution Learning. Information Fusion, 66, 64-75. https://doi.org/10.1016/j.inffus.2020.08.024

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life... Read More about Decomposition-Fusion for Label Distribution Learning.

Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data (2020)
Journal Article
Maillo, J., Triguero, I., & Herrera, F. (2020). Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data. IEEE Access, 1-1. https://doi.org/10.1109/access.2020.2991800

It is recognized the importance of knowing the descriptive properties of a dataset when tackling a data science problem. Having information about the redundancy, complexity and density of a problem allows us to make decisions as to which data preproc... Read More about Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data.

A Local Search with a Surrogate Assisted Option for Instance Reduction (2020)
Conference Proceeding
Neri, F., & Triguero, I. (2020). A Local Search with a Surrogate Assisted Option for Instance Reduction. In Applications of Evolutionary Computation (578-594). https://doi.org/10.1007/978-3-030-43722-0_37

© 2020, Springer Nature Switzerland AG. In data mining, instance reduction is a key data pre-processing step that simplifies and cleans raw data, by either selecting or creating new samples, before applying a learning algorithm. This usually yields t... Read More about A Local Search with a Surrogate Assisted Option for Instance Reduction.

Galaxy Image Classification Based on Citizen Science Data: A Comparative Study (2020)
Journal Article
Jiménez, M., Torres Torres, M., John, R., & Triguero, I. (2020). Galaxy Image Classification Based on Citizen Science Data: A Comparative Study. IEEE Access, 8, 47232-47246. https://doi.org/10.1109/access.2020.2978804

Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers h... Read More about Galaxy Image Classification Based on Citizen Science Data: A Comparative Study.

Multigranulation Super-Trust Model for Attribute Reduction (2020)
Journal Article
Ding, W., Pedrycz, W., Triguero, I., Cao, Z., & Lin, C. (2020). Multigranulation Super-Trust Model for Attribute Reduction. IEEE Transactions on Fuzzy Systems, 29(6), 1395-1408. https://doi.org/10.1109/tfuzz.2020.2975152

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information... Read More about Multigranulation Super-Trust Model for Attribute Reduction.

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems (2020)
Book Chapter
Angarita-Zapata, J. S., Masegosa, A. D., & Triguero, I. (2020). Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems. In O. Llanes Santiago, C. Cruz Corona, A. J. Silva Neto, & J. L. Verdegay (Eds.), Computational intelligence in emerging technologies for engineering applications (187-204). Springer. https://doi.org/10.1007/978-3-030-34409-2_11

© Springer Nature Switzerland AG 2020. Traffic forecasting is a well-known strategy that supports road users and decision-makers to plan their movements on the roads and to improve the management of traffic, respectively. Current data availability an... Read More about Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems.