1,721,107 research outputs found
A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers
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Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach
No full textA Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems
No full textMulti-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets
No full textIn this paper, we show an experimental study on a set of evolutionary fuzzy classifiers (EFCs) purposely designed to manage imbalanced datasets. Three of these EFCs represent the state-of-the-art of the main approaches to the evolutionary generation of fuzzy rule-based systems for imbalanced dataset classification. The fourth EFC is an extension of a multi-objective evolutionary learning (MOEL) scheme we have recently proposed for managing imbalanced datasets: the rule base and the membership function parameters of a set of FRBCs are concurrently learned by optimizing the sensitivity, the specificity and the complexity. By using non-parametric tests, we first compare the results obtained by the four EFCs in terms of area under the ROC curve. We show that our MOEL scheme outperforms two of the comparison algorithms and results to be statistically equivalent to the third. Further, the classifiers generated by our MOEL scheme are characterized by a lower number of rules than the ones generated by the other approaches. To validate the effectiveness of our MOEL scheme in dealing with imbalanced datasets, we also compare our results with the ones achieved, after rebalancing the datasets, by two state-of-the-art algorithms, namely FURIA and FARC-HD, proposed for generating fuzzy rule-based classifiers for balanced datasets. We show that our MOEL scheme is statistically equivalent to FURIA, which is associated with the highest accuracy rank in the statistical tests. However, the rule bases generated by FURIA are characterized by a low interpretability. Finally, we show that the results achieved by our MOEL scheme are statistically equivalent to the ones achieved by four state-of-the-art approaches, based on ensembles of non-fuzzy classifiers, appropriately designed for dealing with imbalanced dataset
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