Vasile Palade
Dr Vasile Palade
Themes:
Interests
My research interests are in the area of Computational Intelligence, encompassing hybrid intelligent systems, neural networks, fuzzy and neuro-fuzzy systems, various nature inspired algorithms (e.g., genetic algorithms, swarm optimization), ensembles of classifiers. Application areas include a wide range of Bioinformatics problems, fault diagnosis, web usage mining, process modelling and control, among others.
Biography
I have joined the Computing Laboratory, Oxford University (now the Department of Computer Science) as a Departmental Lecturer in October 2001. Before joining the Computing Laboratory, I have worked as a Research Fellow with the Department of Engineering, University of Hull, UK, and as an Associate Professor with the Department of Computer Science and Engineering, University of Galati, Romania. I have obtained my PhD at the University of Galati and my BEng/MEng in Computer Engineering at the Technical University of Bucharest, Romania.
Selected Publications
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Class Imbalance Learning Methods for Support Vector Machines
R. Batuwita and V. Palade
In “Imbalanced Learning: Foundations‚ Algorithms‚ and Applications”‚ Haibo He and Yunqian Ma (Eds.)‚ Wiley‚ (book chapter). 2013.
Details about Class Imbalance Learning Methods for Support Vector Machines | BibTeX data for Class Imbalance Learning Methods for Support Vector Machines | Download (pdf) of Class Imbalance Learning Methods for Support Vector Machines
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An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics
V. Lopez‚ A. Fernandez‚ S. Garcia‚ V. Palade and F. Herrera
In Information Sciences. 2013.
in press 2013
Details about An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics | BibTeX data for An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics | Link to An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics
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Solving the Power Economic Dispatch Problem with Generator Constraints by Random Drift Particle Swarm Optimization
J. Sun‚ V. Palade‚ X. Wu‚ W. Fang and Z. Wang
In IEEE Trans. on Industrial Informatics. 2013.
in press‚ available online
Details about Solving the Power Economic Dispatch Problem with Generator Constraints by Random Drift Particle Swarm Optimization | BibTeX data for Solving the Power Economic Dispatch Problem with Generator Constraints by Random Drift Particle Swarm Optimization | Link to Solving the Power Economic Dispatch Problem with Generator Constraints by Random Drift Particle Swarm Optimization