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Applied Sciences (Switzerland)(1)
Journal of Biomedical Informatics(1)
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Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020(1)
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scopus(4)
Finding Associations among Chronic Conditions by Bootstrap and Multiple Correspondence Analysis
Conference ObjectAbstract: Contemporary societies are suffering from negative population growth, with the consequent populationPalabras claves:bootstrap resampling, Chronic conditions, CORRESPONDENCE ANALYSIS, feature selectionAutores:Alonso-Arteaga N., Jose Luis Rojo-Álvarez, Lopez-Fajardo I.C., Mora-Jimnez I., Muoz-Romero S., Rubio-Sánchez M., Soguero-Ruiz C.Fuentes:scopusInformative variable identifier: Expanding interpretability in feature selection
ArticleAbstract: There is nowadays an increasing interest in discovering relationships among input variables (also caPalabras claves:classification, Explainable machine learning, feature selection, interpretability, ResamplingAutores:Gorostiaga A., Jose Luis Rojo-Álvarez, Mora-Jimnez I., Muoz-Romero S., Soguero-Ruiz C.Fuentes:scopusPbkp_redicting colorectal surgical complications using heterogeneous clinical data and kernel methods
ArticleAbstract: Objective: In this work, we have developed a learning system capable of exploiting information convePalabras claves:clinical decision support, Colorectal cancer, Electronic health records, feature selection, Heterogeneous clinical data, kernel methodsAutores:Augestad K.M., Godtliebsen F., Hindberg K., Jenssen R., Jose Luis Rojo-Álvarez, Lindsetmo R.O., Mora-Jimnez I., Mortensen K., Revhaug A., Skrøvseth S., Soguero-Ruiz C.Fuentes:scopuson the differential analysis of enterprise valuation methods as a guideline for unlisted companies assessment (II): Applying machine-learning techniques for unbiased enterprise value assessment
ArticleAbstract: The search for an unbiased company valuation method to reduce uncertainty, whether or not it is autoPalabras claves:Bagging trees, Boosting Trees, Company valuation, Decision Trees, enterprise value, feature selection, Machine learning, Non-linear regression techniques, Supervised techniques, Supported vector machineAutores:Germania Vayas-Ortega, Gimeno-Blanes F.J., Jose Luis Rojo-Álvarez, Rodríguez-Ibáñez M., Soguero-Ruiz C.Fuentes:scopus