A5081322718- Rough Sets and Fuzzy Logic
- Imbalanced Data Classification Techniques
- Image Retrieval and Classification Techniques
- Data Mining Algorithms and Applications
- Machine Learning and Data Classification
- Text and Document Classification Technologies
- Electricity Theft Detection Techniques
- Multi-Criteria Decision Making
- Machine Learning and Algorithms
- Advanced Multi-Objective Optimization Algorithms
- Face and Expression Recognition
- Fuzzy Logic and Control Systems
- Evolutionary Algorithms and Applications
- Video Analysis and Summarization
- Music and Audio Processing
- Machine Learning in Bioinformatics
- Financial Distress and Bankruptcy Prediction
- Advanced Image and Video Retrieval Techniques
- Data Management and Algorithms
- Biomedical Text Mining and Ontologies
Ghent University
2014-2019
VIB-UGent Center for Inflammation Research
2015-2019
Universidad de Granada
2016-2019
Imbalanced classification deals with learning from data a disproportional number of samples in its classes. Traditional classifiers exhibit poor behavior when facing this kind because they do not take into account the imbalanced class distribution. Four main kinds solutions exist to solve problem: modifying distribution, algorithm for considering imbalance representation, including use costs samples, and ensemble methods. In paper, we adopt second type solution introduce that uses fuzzy...
Classification techniques in the big data scenario are high demand a wide variety of applications. The huge increment available may limit applicability most standard techniques. This problem becomes even more difficult when class distribution is skewed, topic known as imbalanced classification. Evolutionary undersampling have shown to be very promising solution deal with imbalance problem. However, their practical application limited problems no than tens thousands instances. In this...
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...
Multi-instance learning is a setting in supervised where the data consist of bags instances. Samples dataset are groups individual In classification problems, decision value assigned to entire bag, and an unseen bag involves prediction based on instances it contains. this paper, we develop framework for multi-instance classifiers fuzzy set theory. Fuzzy sets have been used many machine applications, but so far not data. We explore its untapped potential here. interpret classes as determine...
Size and complexity of Big Data requires advances in machine learning algorithms to adequately learn from such data. While distributed shared-nothing architectures (Hadoop/Spark) are becoming increasingly popular develop new algorithms, it is quite challenging adapt existing algorithms. In this paper, we propose a solution for big data regression, where the aim regression model over large high-dimensional datasets. First, implementation weighted kNN method presented followed by novel...
Semi-supervised learning incorporates aspects of both supervised and unsupervised learning. In semi-supervised classification, only some data instances have associated class labels, while others are unlabelled. One particular group classification approaches those known as self-labelling techniques, which attempt to assign labels the unlabelled instances. This is achieved by using predictions based upon information labelled part data. this paper, applicability suitability fuzzy rough set...