Browsing by Author "Carmona, C. J."

Now showing 1 - 9 of 9
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    Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems
    (DMU, 2012-09-01) Carmona, C. J.; Elizondo, David
    Nowadays, there is a increasing in the use of learning management systems from the universities. This type of systems are also known under other di erent terms as course management systems or learning content management systems. Speci cally, these systems are e-learning platforms o ering di erent facilities for information sharing and communication between the participants in the e-learning process. This contribution presents an experimental study with several subgroup discovery algorithms based on evolutionary fuzzy systems using data from a web-based education system. The main objective of this contribution is to extract unusual subgroups to describe possible relationships between the use of the e-learning platform and marks obtained by the students. The results obtained by the best performing algorithm, NMEEF-SD, are also presented. The most representative results obtained by this algorithm are summarised in order to obtain knowledge that can allow teachers to take actions to improve student performance.
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    E2PAMEA: un algoritmo evolutivo para la extraccion eficiente de patrones emergentes difusos en entornos big data
    (DMU, 2021) Garcia-Vico, A. M.; Elizondo, David; Charte, F.; Gonzalez, P.; Carmona, C. J.
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    A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans
    (Elsevier, 2015-03) Carmona, C. J.; Ruiz-Rodado, Victor; del Jesus, M. J.; Weber, A.; Grootveld, M.; Gonzalez, P.; Elizondo, David
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    Fuzzy rules for describing subgroups from Influenza A virus using a multi-objective evolutionary algorithm
    (Elsevier, 2013) Carmona, C. J.; Chrysostomou, C.; Seker, H.; del Jesus, M. J.
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    An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects
    (Wiley, 2017-10-18) Carmona, C. J.; Garcia-Vico, A.M.; Martin, D.; Garcia-Borroto, M.; del Jesus, M. J.
    Emerging pattern mining is a data mining task that aims to discover discriminative patterns, which can describe emerging behavior with respect to a property of interest. In recent years, the description of datasets has become an interesting field due to the easy acquisition of knowledge by the experts. In this review, we will focus on the descriptive point of view of the task. We collect the existing approaches that have been proposed in the literature and group them together in a taxonomy in order to obtain a general vision of the task. A complete empirical study demonstrates the suitability of the approaches presented. This review also presents future trends and emerging prospects within pattern mining and the benefits of knowledge extracted from emerging patterns.
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    Subgroup Discovery trhough Evolutionary Fuzzy Systems applied to Bioinformatic problems
    (Technical Report DMU, 2011-03-01) Elizondo, David; Carmona, C. J.
    Subgroup discovery is a descriptive data mining technique using supervised learning. This paper presents a summary about the main properties and elements about subgroup discovery task. In addition, we will focus on the suitability and potential of the search performed by evolutionary algorithms in order to apply in the development of subgroup discovery algorithms, and in the use of fuzzy logic which is a soft computing technique very close to the human reasoning. The hybridisation of both techniques are well known as evolutionary fuzzy system. The most relevant applications of evolutionary fuzzy systems for subgroup discovery in the bioinformatics domains are outlined in this work. Specifically, these algorithms are applied to a problem based on the Influenza A virus and the accute sore throat problem.
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    Subgroup Discovery: Real-World Applications
    (Techincal Report, 2011-03-01) Carmona, C. J.; Elizondo, David
    Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. In this paper, an overview about subgroup discovery is performed. In addition, di erent real-world applications solved through evolutionary algorithms where the suitability and potential of this type of algorithms for the development of subgroup discovery algorithms are presented.
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    Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art
    (DMU, 2016-06-01) Carmona, C. J.; Elizondo, David
    The supervised descriptive rule discovery concept groups a set of data mining techniques whose objective is to describe data with respect to a property of interest. Among the techniques within this concept are the subgroup discovery, emerging patterns and contrast sets. This contribution presents the supervised descriptive rule discovery concept within the data mining literature. Specifically, it is important to remark the main di erence with respect to other existing techniques within classification or description. In addition, a a survey of the state-of-the-art about the different techniques within supervised descriptive rule discovery throughout the literature can be observed. The paper allows to the experts to analyse the compatibilities between terms and heuristics of the different data mining tasks within this concept.
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    Urinary Metabolic Distinction of Niemann--Pick Class 1 Disease through the Use of Subgroup Discovery
    (MDPI, 2023-10-13) Carmona, C. J.; German-Morales, M.; Elizondo, David; Ruiz-Rodado, V.; Grootveld, M.
    In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann–Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved ‘normal’ (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised ‘normal’ 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and ‘biomarkers’ featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with ‘normal’ levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan–nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.