Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Statistics for Spatial Data.

a b s t r a c t The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1374_2011-Derrac-SWEVO.pdf

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results.

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

The use of knowledge-based systems can represent an efficient approach for system management, providing automatic control strategies with Artificial Intelligence capabilities. By means of Artificial Intelligence, the system is capable of assessing, diagnosing and suggesting the best operation mode. One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising the expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with conclusions. However, this way to work sometimes fails to obtain an optimal behavior. To solve this problem, within the framework of Machine Learning, some artificial intelligence techniques could be applied to enhance the controller behavior. In this work, a post-processing method is used to obtain more compact and accurate fuzzy logic controllers. This method combines a new technique to perform an evolutionary lateral tuning of the linguistic variables with a simple technique for rule selection (that removes unnecessary rules). To do so, the tuning technique considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved linguistic labels.

Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection

Background: Puberty is a time of considerable metabolic and hormonal changes associated with a physiological increase in peripheral tissue insulin resistance (IR). There is evidence that physiological IR does not resolve in youth who are obese, which may result in increased cardio-metabolic risk. Understanding the molecular and biological processes underlying the development of IR in puberty and the additional impact of obesity on these processes is crucial to prevent type 2 diabetes. Methods: This is a longitudinal study based on the follow-up until puberty of a cohort of prepubertal Spanish boys and girls. The study population was composed of 139 children organized in a longitudinal approach of 90 subjects (47 females) and two cross-sectional approaches of 99 (52 females) and 130 (71 females) subjects for prepubertal and pubertal stages, respectively. Children were allocated into experimental groups according to their obesity and IR status before and after the onset of puberty. All participants presented blood DNA samples for GWAS and EWAS analyses. In 44 children of the pubertal stage, we counted on blood RNA samples for RNA-seq analysis. Results: Our large-scale integrative molecular analysis identified novel blood multi-omics signatures (mapping the loci ABCG1, ESR1 and VASN, among others) significantly associated with IR longitudinal trajectories in children with obesity during pubertal maturation. Functional enrichment analysis revealed that identified loci participate in systemic metabolic pathways and sexual maturation processes relevant to the pathogenesis of IR in the context of puberty. Additional analyses on cardiometabolic and inflammatory phenotypes showed that blood DNAm patterns of some of the identified loci are further associated, beyond IR, with an overall risky-cardiometabolic profile in children. Serum protein levels of vasorin (VASN), one of the most promising novel biomarkers identified in this study, were further associated with IR in the pubertal All rights reserved. No reuse allowed without permission. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this this version posted December 16, 2022. ; https://doi.org/10.1101/2022.12.13.22283415 doi: medRxiv preprint

Title: Integrative analysis of blood cells DNA methylation, transcriptomics and genomics identifies novel epigenetic regulatory mechanisms of insulin resistance during puberty in children with obesity Short running title: Multi-omics signatures of insulin resistance during puberty in children with o

A large number of systems with a great predictive capacity, such as Deep Learning, are being currently used to solve a wide variety of real problems. However, the models obtained are not easy to understand by scientists, giving rise to the field of eXplainable Artificial Intelligence, which encourage techniques that obtain accurate and understandable models. Fuzzy Association Rules are models that can be understood by themselves, but its interpretability can be improved by representing the same information with fewer and simpler rules. In this work, we propose Meta-Fuzzy Items, which allows to define more generic fuzzy items to represent the same information with fewer rules, and to extend the type of associations that can be represented. Based on this proposal, a new fuzzy data-mining algorithm is presented to extract interesting and interpretable rules from quantitative transactions. The quality of our approach is analyzed using statistical analysis and comparing with a well-known fuzzy data-mining algorithm.

Meta-Fuzzy Items for Fuzzy Association Rules

The use of knowledge-based systems can represent an efficient approach for system management, providing automatic control strategies with Artificial Intelligence capabilities. By means of Artificial Intelligence, the system is capable of assessing, diagnosing and suggesting the best operation mode. One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising the expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with conclusions. However, this way to work sometimes fails to obtain an optimal behavior. To solve this problem, within the framework of Machine Learning, some artificial intelligence techniques could be applied to enhance the controller behavior.

In this work, a post-processing method is used to obtain more compact and accurate fuzzy logic controllers. This method combines a new technique to perform an evolutionary lateral tuning of the linguistic variables with a simple technique for rule selection (that removes unnecessary rules). To do so, the tuning technique considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved linguistic labels.

/pdf/improving-fuzzy-rule-based-decision-models-by-means-of-a-2l6y4rvxpw.pdf

Jesús Alcalá-Fdez

Papers

Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection

Title: Integrative analysis of blood cells DNA methylation, transcriptomics and genomics identifies novel epigenetic regulatory mechanisms of insulin resistance during puberty in children with obesity Short running title: Multi-omics signatures of insulin resistance during puberty in children with o

Meta-Fuzzy Items for Fuzzy Association Rules

Improving fuzzy rule-based decision models by means of a genetic 2-tuples based tuning and the rule selection