Research Staff

Contact

3rd floor, Wolfson House, 4 Stephenson Way
University College London


NW1 2HE
United Kingdom
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Miscellaneous Information

 

 I obtained my M.Sc. degree in Computer Engineering and my Ph.D. degree in Information Engineering from the University of Pisa (Italy), in 2003 and 2007, respectively. I was a research fellow of the Computational Intelligence Group at the Department of Information Engineering, University of Pisa from 2008 to 2014.Currently, I am a research associate in the Centre for Medical Image Computing at University College London.

My main research interests are in the area of Computational Intelligence, with particular emphasis to fuzzy systems and multi-objective evolutionary algorithms. I am also interested in Neural Networks design for pattern recognition and regression problems, and in classification system design for imbalanced and cost-sensitive datasets.

 

PUBLICATIONS

  • M. Antonelli, P. Ducange, F. Marcelloni, Multi-objective Evolutionary Design of Fuzzy Rule-based Systems”, Handbook on Computational Intelligence, Edited by P. Angelov, World Scientific, 2016, pp. 627-662.

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2015), Multi-objective evolutionary design of granular rule-based classifiers,  Granular Computing, vol.1, pp 37-58

  • M. Antonelli, P. Ducange, F. Marcelloni, A. Segatori, (2015), On the Influence of Feature Selection in Fuzzy Rule-based Regression Model Generation,”  Information Sciences Elsevier Vol. 329, 2016,  pp. 649-669.

  • M. Antonelli, D. Bernardo, H. Hagras, F. Marcelloni, (2015) Multi-Objective Evolutionary Optimization of Type-2 Fuzzy Rule-based Systems for Financial Data Classification,  IEEE Transactions on Fuzzy Systems, accepted with minor revision.

  • Michela Antonelli, Pietro Ducange, Francesco Marcelloni, Armando Segatori, (2015): A novel associative classification model based on a fuzzy frequent pattern mining algorithm. Expert Systems with Applications, vol. 42, n. 4, p. 2086–2097.

  • P. Ducange, F. Marcelloni, M. Antonelli (2014): A Novel Approach Based on Finite-State Machines with Fuzzy Transitions for Nonintrusive Home Appliance Monitoring. IEEE Trans. Industrial Informatics vol. 10, n. 2, p. 1185-1197.

  • M. Antonelli, P. Ducange, F. Marcelloni, (2014): An Experimental Study on Evolutionary Fuzzy Classifiers Designed for Managing Imbalanced Datasets. Neurocomputing, vol. 146, p.125-136.

  • Pietro Ducange, Francesco Marcelloni, Michela Antonelli (2014): A Fast and Efficient Multi-Objective Evolutionary Learning Scheme for Fuzzy Rule-based Classifiers. Information Science, vol. 283, p. 36-54.

  • M. Antonelli, P. Ducange, F. Marcelloni (2013): An Efficient Multi-Objective Evolutionary Fuzzy System for Regression Problems. International Journal of Approximate Reasoning, vol 54, n. 9, p. 1434-1451.

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2013). A CAD System for Detecting Lung Nodules in CT Scans based on Multi-Objective Evolutionary Fuzzy Classifier. In Proc. Of MIBISOC (Medical Imaging Using Bio-inspired Soft Computing) Final Conference, Brussels, Belgium.

  • Antonelli M, Ducange P, Marcelloni F, Segatori A (2013). Evolutionary Fuzzy Classifier for Imbalanced Datasets: An experimental Comparison, in Proc. Of 2013 IFSA-NAFIPS Joint Conference, pp. 13-18, Edmonton, Canada, 2013

  • M. Antonelli, P. Ducange, F. Marcelloni, Feature Selection based on Fuzzy Mutual Information, in Proc. of International Workshop on Fuzzy Logic and Applications (WILF) 2013. Genova, Italy, 2013

  • G. Anastasi, M. Antonelli, A. Bechini, S. Brienza, E. D’Andrea, D. De Guglielmo, P. Ducange, B. Lazzerini, F. Marcelloni, A. Segatori, Urban and Social Sensing for Sustainable Mobility in Smart Cities, in Proc. of IFIP Third Conference on Sustainable Internet and ICT for Sustainability, Palermo, Italy, 2013

  • Antonelli M, Ducange P, Marcelloni F (2012). Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach. IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 20, p. 276-290.

  • Antonelli M, Ducange P, Marcelloni F (2012). Multi-objective Evolutionary Rule and Condition Selection for Designing Fuzzy Rule-based Classifiers. In: Proceedings of the IEEE World Congress on Computational Intelligence - Fuzz-IEEE. IEEE INTERNATIONAL FUZZY SYSTEMS CONFERENCE PROCEEDINGS, 6251174, ISBN: 978-146731506-7, ISSN: 1544-5615, Brisbane, Australia, 10-15 June, doi: 10.1109/FUZZ-IEEE.2012.6251174

  • Antonelli M, Cococcioni M, Lazzerini B, Marcelloni F (2011). Computer-aided detection of lung nodules based on decision fusion techniques. PATTERN ANALYSIS AND APPLICATIONS, vol. 14, p. 295-310, ISSN: 1433-7541, doi: 10.1007/s10044-011-0219-9

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2011). Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index. SOFT COMPUTING, vol.15, p. 1981-1998, ISSN: 1432-7643, doi: 10.1007/s00500-010-0629-4

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2011). Learning knowledge bases of multi-objective evolutionary fuzzy systems by simultaneously optimizing accuracy, complexity and partition integrity. SOFT COMPUTING, vol. 15, p. 2335-2354, ISSN: 1432-7643.

  • Antonelli M, Ducange P, Marcelloni F (2011). A New Approach to Handle High Dimensional and Large Datasets in Multi-objective Evolutionary Fuzzy Systems. In: Proceedings of the 2011 IEEE International Conference on Fuzzy Systems. IEEE INTERNATIONAL FUZZY SYSTEMS CONFERENCE PROCEEDINGS, p. 1286-1293, ISBN: 978-142447317-5, ISSN: 1544-5615, Taipei, 27 June, doi: 10.1109/FUZZY.2011.6007610

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2011). Multi-objective Evolutionary Generation of Mamdani Fuzzy Rule-Based Systems based on Rule and Condition Selection. In: Proceedings of the 5th IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems. p. 47-53, ISBN: 978-161284050-5, Paris- FRANCE, 11 April 2011, doi: 10.1109/GEFS.2011.5949489

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2010). Exploiting a Three-Objective Evolutionary Algorithm for Generating Mamdani Fuzzy Rule-Based Systems. In: 2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010). IEEE INTERNATIONAL FUZZY SYSTEMS CONFERENCE PROCEEDINGS, 5583965, ISBN: 978-1-4244-6920-8, ISSN: 1544-5615, Barcelona, SPAIN, JUL 18-23, 2010, doi: 10.1109/FUZZY.2010.5583965

  • Antonelli M, Ducange P, Marcelloni F (2010). Exploiting a Coevolutionary Approach to Concurrently Select Training Instances and Learn Rule Bases of Mamdani Fuzzy Systems. In: 2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010). IEEE INTERNATIONAL FUZZY SYSTEMS CONFERENCE PROCEEDINGS, p. 1-7, ISBN: 978-142446920-8, ISSN: 1544-5615, doi: 10.1109/FUZZY.2010.5584292

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009). Multi-objective Evolutionary Learning of Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Systems. EVOLUTIONARY INTELLIGENCE, vol. 2, p. 21-37, ISSN: 1864-5909, doi: 10.1007/s12065-009-0022-3

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009). Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, vol. 50, p. 1066-1080, ISSN: 0888-613X, doi: 10.1016/j.ijar.2009.04.004

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009). Learning Concurrently Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Systems. In: PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE. P 1033-1038, ISBN: 978-989-95079-6-8, Lisbon, PORTUGAL, JUL 20-24, 2009

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009). Exploiting a New Interpretability Index in the Multi-Objective Evolutionary Learning of Mamdani Fuzzy Rule-Based Systems. In: Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, 2009 (ISDA '09). 5364732, ISBN: 978-076953872-3, Pisa - Italy, 30 November 2009, doi: 10.1109/ISDA.2009.166

  • Volpi S L, Antonelli M, Lazzerini B, Marcelloni F, Stefanescu Dan C (2009). Segmentation and reconstruction of the lung and the mediastinum volumes in CT images. In: IEEE 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL). p. 71-76, ISBN: 978-142444641-4, Bratislava, Slovak Republic, Nov. 2009, doi: 10.1109/ISABEL.2009.5373701

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2009). A Three-Objective Evolutionary Approach to Generate Mamdani Fuzzy Rule-Based Systems. In: HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS. LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, vol. 5572, p. 613-620, ISBN: 978-3-642-02318-7, ISSN: 0302-9743, Salamanca, SPAIN, JUN 10-12, 2009, doi: 10.1007/978-3-642-02319-4_74

  • Antonelli M, Cococcioni M, Lazzerini B, Marcelloni F, Stefanescu D (2008). A multi-classifier system for pulmonary nodule classification. In: Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, 2008 (CBMS '08). p. 587-589, ISBN: 978-076953165-6, Jyvaskyla-Finland, 17-19 June, doi: 10.1109/CBMS.2008.70

  • Antonelli M, Ducange P, Lazzerini B, Marcelloni F (2008). A multi-objective genetic approach to concurrently learn partition granularity and rule bases of Mamdani fuzzy systems. In: Proceedings of the Eighth International Conference on Hybrid Intelligent Systems, 2008 (HIS '08). p. 278-283, ISBN: 978-076953326-1, Barcelona, Spain, September 10-12, 2008, doi: 10.1109/HIS.2008.93

  • Antonelli M, Cococcioni M, Frosini G, Lazzerini B, Marcelloni F (2007). Modelling a team of radiologists for lung nodule detection in CT scans. In: Proceedings of the 11th Conference on KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS 2007. LECTURE NOTES IN COMPUTER SCIENCE, vol. 4692, p. 303-310, ISBN: 978-3-540-74817-5, ISSN: 0302-9743, Vietri sul Mare, ITALY, SEP 12-14, 2007, doi: 10.1007/978-3-540-74819-9_38

  • Antonelli M, Frosini G, Lazzerini B, Marcelloni F (2006). Automated detection of pulmonary nodules in CT scans. WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, p. 799-803, ISSN: 2010-376X

  • Antonelli M, Yang G-Z (2006). Lung nodule detection using eye-tracking . In: Proceedings of the 14th IEEE International Conference on Image Processing. vol. 2, p. 457-460, ISBN: 978-142441437-6, San Antonio, Texas, September 16-19, 2007, doi: 10.1109/ICIP.2007.4379191

  • Antonelli M, Frosini G, Lazzerini B, Marcelloni F (2006). A CAD system for lung nodule detection based on an anatomical model and a fuzzy neural network. In: NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society. p. 469-474, ISBN: 978-1-4244-0362-2, doi: 10.1109/NAFIPS.2006.365451

  • Antonelli M, Frosini G, Lazzerini B, Marcelloni F (2005). Automated detection of pulmonary nodules in CT scans. In: Proceedings of 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC 2005). vol. 2, p. 799-803, ISBN: 978-076952504-4, Vienna- Austria, 28-30 November

  • Antonelli M, Lazzerini B, Marcelloni F (2005). Segmentation and reconstruction of the lung volume in CT images. In: Proceedings of the 2005 ACM Symposium on Applied Computing. vol. 1, p. 255-259, Santa Fe, New Mexico, doi: 10.1145/1066677.1066738

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