All Outputs (3)

Vehicle incident hot spots identification: An approach for big data (2017)
Conference Proceeding
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017). Vehicle incident hot spots identification: An approach for big data. In Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications; 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE); and 14th IEEE International Conference on Embedded Software and Systems, (901-908). https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a nu... Read More about Vehicle incident hot spots identification: An approach for big data.

Exact fuzzy k-Nearest neighbor classification for big datasets (2017)
Conference Proceeding
Maillo, J., Luengo, J., García, S., Herrera, F., & Triguero, I. (2017). Exact fuzzy k-Nearest neighbor classification for big datasets.

The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same import... Read More about Exact fuzzy k-Nearest neighbor classification for big datasets.

A first attempt on global evolutionary undersampling for imbalanced big data (2017)
Conference Proceeding
Triguero, I., Galar, M., Bustince, H., & Herrera, F. (2017). A first attempt on global evolutionary undersampling for imbalanced big data.

The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are i... Read More about A first attempt on global evolutionary undersampling for imbalanced big data.