A5043305270- Rough Sets and Fuzzy Logic
- Data Mining Algorithms and Applications
- Multi-Criteria Decision Making
- Advanced Algebra and Logic
- Traffic Prediction and Management Techniques
- Data Stream Mining Techniques
- Elevator Systems and Control
- Data Management and Algorithms
- Green IT and Sustainability
- Supply Chain and Inventory Management
- Semantic Web and Ontologies
- Logic, Reasoning, and Knowledge
- Fuzzy Systems and Optimization
- Imbalanced Data Classification Techniques
- Time Series Analysis and Forecasting
University of Antwerp
2023-2024
Ghent University
2013-2019
Ghent University Hospital
2015
Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed use fuzzy rough set theory the development of new techniques tackling these characteristics. Fuzzy sets deal with data, while allow model incomp lete As such, hybrid setting two paradigms an ideal candidate tool confront separate challenges. In this paper, we present a thorough review on applications. We recall their integration preprocessing methods consider...
In this paper we review four fuzzy extensions of the socalled tight pair covering based rough set approximation operators.Furthermore, propose two new pair: for first model, apply technique representation by levels to define operators, while second model is an intuitive extension crisp operators.For six models, study which theoretical properties they satisfy.Moreover, discuss interrelationships between models.
One of the key challenges for (fresh produce) retailers is achieving optimal demand forecasting, as it plays a crucial role in operational decision-making and dampens Bullwhip Effect. Improved forecasts holds potential to achieve balance between minimizing waste avoiding shortages. Different have partial views on same products, which—when combined—can improve forecasting individual retailers' inventory demand. However, are hesitant share all their data. Therefore, we propose an end-to-end...
Forecasting is the process of predicting future events or values based on past data to support, for instance, strategic business decisions. This historical often takes form multivariate time series. Recently, graph networks emerged as a powerful forecasting framework by considering spatiotemporal relations in data. In this paper, we describe adoption model built an encoder-decoder architecture. The encoder projects into lower dimension subspace, whereas decoder uses generated embeddings...