M. Antonelli

发表

Multi-objective evolutionary design of granular rule-based classifiers

P. Ducange, F. Marcelloni, B. Lazzerini, 2016 .

A Feature Selection Method based on Fuzzy Mutual Information for Fuzzy Rule-based Regression Models

P. Ducange, F. Marcelloni, M. Antonelli, 2014 .

A CAD System for Detecting Lung Nodules in CT Scans based on Multi-Objective Evolutionary Fuzzy Classifiers

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2013, SOCO 2013.

Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

Hani Hagras, Michela Antonelli, Francesco Marcelloni, 2017, IEEE Transactions on Fuzzy Systems.

A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2014, Inf. Sci..

An efficient multi-objective evolutionary fuzzy system for regression problems

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2013, Int. J. Approx. Reason..

Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2012, IEEE Transactions on Fuzzy Systems.

Learning knowledge bases of multi-objective evolutionary fuzzy systems by simultaneously optimizing accuracy, complexity and partition integrity

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2011, Soft Comput..

Learning concurrently data and rule bases of Mamdani fuzzy rule-based systems by exploiting a novel interpretability index

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2011, Soft Comput..

Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2009, Evol. Intell..

Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2009, Int. J. Approx. Reason..

A Multi-Objective Genetic Approach to Concurrently Learn Partition Granularity and Rule Bases of Mamdani Fuzzy Systems

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

On the influence of feature selection in fuzzy rule-based regression model generation

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2016, Inf. Sci..

Learning Concurrently Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Systems

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2009, IFSA/EUSFLAT Conf..

A Novel Approach Based on Finite-State Machines with Fuzzy Transitions for Nonintrusive Home Appliance Monitoring

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2014, IEEE Transactions on Industrial Informatics.

Multi-objective evolutionary generation of Mamdani fuzzy rule-based systems based on rule and condition selection

Beatrice Lazzerini, Michela Antonelli, Pietro Ducange, 2011, 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).

A novel associative classification model based on a fuzzy frequent pattern mining algorithm

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2015, Expert Syst. Appl..

Lung Nodule Detection using Eye-Tracking

Guang-Zhong Yang, Michela Antonelli, Guang-Zhong Yang, 2007, 2007 IEEE International Conference on Image Processing.

A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2017, Inf. Sci..

Urban and social sensing for sustainable mobility in smart cities

Giuseppe Anastasi, Eleonora D'Andrea, Beatrice Lazzerini, 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

Exploiting a coevolutionary approach to concurrently select training instances and learn rule bases of Mamdani fuzzy systems

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2010, International Conference on Fuzzy Systems.

An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets

Michela Antonelli, Pietro Ducange, Francesco Marcelloni, 2014, Neurocomputing.

Multi-objective evolutionary design of granular rule-based classifiers

P. Ducange, F. Marcelloni, B. Lazzerini, 2015, Granular Computing.

Segmentation and reconstruction of the lung and the mediastinum volumes in CT images

Beatrice Lazzerini, Francesco Marcelloni, Dan C. Stefanescu, 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.