Francisco Herrera

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

2018 · Journal of Artificial Intelligence Research

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well …

DPASF: a flink library for streaming data preprocessing

2019 · Big Data Analytics

Data preprocessing techniques are devoted to correcting or alleviating errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data …

Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid.

2020 · arXiv (Cornell University)

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural …

Federated Fuzzy Learning with Imbalanced Data

2021 · 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

Federated learning (FL) is an emerging and privacy-preserving machine learning technique that is shown to be increasingly important in the digital age. The two challenging issues for FL are: (1) communication overhead between clients and …

Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

2022 · arXiv (Cornell University)

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and …