Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

The authors would like to thank the Spanish children and parents who participated in the study.Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic variants have been widely associated with obesity in children populations. The implication of such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the prediction and pharmacological management of obesity in Spanish children, further investigating its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted on genetics data from three well-characterized children populations (composed of 574, 96 and 124 individuals), following both cross-sectional and longitudinal designs, expanding childhood and puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10−8 ), and that could be used as a good predictor of obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not associated with cardio-metabolic comorbidities in children and that certain environmental factors interact with the genetic predisposition to the disease. Finally, according to the results derived from a weight-reduction metformin intervention in children with obesity, we discarded the utility of the pGRS as a pharmacogenetics marker of metformin response.Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica (I + D + I), Instituto de Salud Carlos III-Health Research Funding (FONDOS FEDER) PI1102042 PI1102059 PI1601301 PI1600871Spanish Ministry of Health, Social and Equality, General Department for Pharmacy and Health Products EC10-243 EC10-056 EC10-281 EC10-227Regional Government of Andalusia ("Plan Andaluz de investigacion, desarrollo e innovacion (2018)") P18-RT-2248Mapfre Foundation ("Research grants by Ignacio H. de Larramendi 2017")Instituto de Salud Carlos III IFI17/0004

Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets a comparative study

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets. Document type: Part of book or chapter of boo

Py4JFML: A Python wrapper for using the IEEE Std 1855-2016 through JFML

JFML is an open source Java library aimed at facilitating interoperability of fuzzy systems by implementing the IEEE Std 1855-2016 - the IEEE Standard for Fuzzy Markup Language (FML) that is sponsored by the IEEE Computational Intelligence Society. We developed a Python wrapper for JFML that enables to use all the functionalities of JFML through a Python 3.x module. The bridge between Python and Java is accomplished through the use of the Py4J framework. As a result, the possibility of using the IEEE standard for representing fuzzy systems is enlarged to a wider community of developers and knowledge engineers, with minimal code redundancy. Experiments show full interoperability between Python programs and JFML without any tangible overhead. We illustrate the use of Py4JFML in a beer style classification case study