@article {8049471, title = {Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming}, journal = {IEEE Transactions on Cybernetics}, volume = {48}, number = {11}, year = {2018}, month = {Nov}, pages = {3030-3044}, abstract = {Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.}, keywords = {Association rules, Computer science, context awareness, context-aware association rules mining, context-sensitive factors, contextual features, contextual information, contextual-sensitive features, data mining, Feature extraction, genetic algorithms, Genetic programming, Grammar, grammar-based genetic programming methodology, grammars, pattern mining process, Proposals, ubiquitous computing}, issn = {2168-2267}, doi = {10.1109/TCYB.2017.2750919}, author = {J. M. Luna and M. Pechenizkiy and M. J. del Jesus and S. Ventura} } @conference {4484560, title = {A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers}, booktitle = {2008 3rd International Workshop on Genetic and Evolving Systems}, year = {2008}, month = {March}, pages = {11-16}, keywords = {Bagging, bagging fuzzy rule-based classification system, Boosting, component classifier, Design methodology, evolutionary computation, fuzzy set theory, Fuzzy systems, genetic algorithms, heuristic fuzzy classification rule generation method, Humans, knowledge based systems, learning (artificial intelligence), machine learning, multicriteria genetic algorithm, pattern classification, Proposals, Scalability}, doi = {10.1109/GEFS.2008.4484560}, author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez} } @conference {4295638, title = {Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems}, booktitle = {2007 IEEE International Fuzzy Systems Conference}, year = {2007}, month = {July}, pages = {1-6}, abstract = {In the design of fuzzy rule-based classification systems (FRBCSs) a feature selection process which determines the most relevant features is a crucial component in the majority of the classification problems. This simplification process increases the efficiency of the design process, improves the interpretability of the FRBCS obtained and its generalization capacity. Most of the feature selection algorithms provide a set of variables which are adequate for the induction process according to different quality measures. Nevertheless it can be useful for the induction process to determine not only a set of variables but also different set of variables. These sets of variables can be used for the design of a set of FRBCSs which can be combined in a multiclassifler system, improving the prediction capacity increasing its description capacity. In this work, different proposals of niching genetic algorithms for the feature selection process are analyzed. The different sets of features provided by them are used in a multiclassifier system designed by means of a genetic proposal. The experimentation shows the adaptation of this type of genetic algorithms to the FRBCS design.}, keywords = {Algorithm design and analysis, classification, data mining, Databases, description capacity, Feature extraction, feature selection algorithms, Fuzzy reasoning, fuzzy rule-based classification systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithms, induction process, Knowledge representation, multiclassifler system, niching genetic algorithms, prediction capacity, Process design, Proposals}, issn = {1098-7584}, doi = {10.1109/FUZZY.2007.4295638}, author = {Jos{\'e} Aguilera and M. Chica and M. J. del Jesus and F. Herrera} } @conference {943783, title = {Genetic tuning of fuzzy rule-based systems integrating linguistic hedges}, booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)}, volume = {3}, year = {2001}, month = {July}, pages = {1570-1574 vol.3}, abstract = {Tuning fuzzy rule-based systems for linguistic modeling is an interesting and widely developed task. It involves adjusting the membership functions composing the knowledge base. To do that, changing the parameters defining each membership function as using linguistic hedges to slightly modify them may be considered. This paper introduces a genetic tuning process for jointly making these two tuning approaches. The experimental results show that our method obtains accurate linguistic models in both approximation and generalization aspects.}, keywords = {Computer science, experimental results, fuzzy logic, Fuzzy rule-based systems, Fuzzy sets, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, genetic tuning process, knowledge base, knowledge based systems, linguistic hedges, linguistic modeling, membership functions, Proposals, Shape, Takagi-Sugeno model, Timing, uncertainty handling}, doi = {10.1109/NAFIPS.2001.943783}, author = {J. Casillas and O. Cord{\'o}n and F. Herrera and M. J. del Jesus} }