Sanz Delgado, José Antonio
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Sanz Delgado
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José Antonio
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Estadística, Informática y Matemáticas
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ISC. Institute of Smart Cities
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Publication Open Access CFM-BD: a distributed rule induction algorithm for building compact fuzzy models in Big Data classification problems(IEEE, 2020) Elkano Ilintxeta, Mikel; Sanz Delgado, José Antonio; Barrenechea Tartas, Edurne; Bustince Sola, Humberto; Galar Idoate, Mikel; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y MatemáticasInterpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule based classifiers have not been able to maintain the good tradeoff between accuracy and interpretability that has characterized these techniques in non-Big-Data environments. The most accurate methods build models composed of a large number of rules and fuzzy sets that are too complex, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: Preprocessing based on the probability integral transform theorem; rule induction inspired by CHI-BD and Apriori algorithms; and rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than three antecedents, employing five linguistic labels for all variables.Publication Open Access An evolutionary underbagging approach to tackle the survival prediction of trauma patients: a case study at the Hospital of Navarre(IEEE, 2019) Sanz Delgado, José Antonio; Galar Idoate, Mikel; Bustince Sola, Humberto; Belzunegui Otano, Tomás; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas; Gobierno de Navarra / Nafarroako Gobernua, PI-019/11Survival prediction systems are used among emergency services at hospitals in order to measure their quality objectively. In order to do so, the estimated mortality rate given by a prediction model is compared with the real rate of the hospital. Hence, the accuracy of the prediction system is a key factor as more reliable estimations can be obtained. Survival prediction systems are aimed at scoring the severity of patients' injuries. Afterward, this score is used to estimate whether the patient will survive or not. Luckily, the number of patients who survive their injuries is greater than that of those who die. However, this degree of imbalance implies a greater difficulty in learning the prediction models. The aim of this paper is to develop a new prediction system for the Hospital of Navarre with the goal of improving the prediction capabilities of the currently used models since it would imply having a more reliable measurement of its quality. In order to do so, we propose a new strategy to conform an ensemble of classifiers using an evolutionary under sampling process in the bagging methodology. The experimental study is carried out over 462 patients who were treated at the Hospital of Navarre. Our new ensemble approach is an appropriate tool to deal with this problem as it is able to outperform the currently used models by the staff of the hospital as well as several state-of-the-art ensemble approaches designed for imbalanced domains.