Supporting preprocessing and postprocessing for machine learning algorithms: a workbench for ID3 |
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| Institution: | 1. Geriatric Intensive Care Medicine, University of Florence and Azienda Ospedaliero Universitaria Careggi, Viale Pieraccini 6, 50139 Florence, Italy;2. Internal and post-surgery Medicine, University of Florence and Azienda Ospedaliero Universitaria Careggi, Florence, Italy;1. Department of Information Technology, Hospital Virgen del Castillo, Yecla, Gerencia Área de Salud V - Altiplano, Servicio Murciano de Salud, Spain;2. Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, España;3. Doctoral student Nursing Department, Faculty of Health Sciences, University of Alicante, Spain;4. Department of Nursing, Faculty of Health Sciences, University of Alicante, Spain;5. Department of Paediatrics, Hospital Virgen del Castillo, Yecla, Gerencia Área de Salud V-Altiplano, Servicio Murciano de Salud, Spain;1. INSERM, Centre de Recherche des Cordeliers, UMRS 1138, Université Paris Descartes, Sorbonne Paris Cité, Paris, France;2. Hôpital Européen Georges Pompidou, Department of Medical Informatics, Assistance Publique – Hôpitaux de Paris (AP-HP), Université Paris Descartes, 20 rue Leblanc, 75015 Paris, France;3. LIRIS UMR CNRS 5205, Université Claude Bernard Lyon 1, Villeurbanne, France;4. Hôpital Necker – Enfants Malades, Department of Medical Informatics, Assistance Publique – Hôpitaux de Paris (AP-HP), Université Paris Descartes, France;5. Hôpital Européen Georges Pompidou, Department of Biochimistry, Assistance Publique – Hôpitaux de Paris (AP-HP), Université Paris Descartes, France;6. Hôpital Européen Georges Pompidou, Department of Hematology, Assistance Publique – Hôpitaux de Paris (AP-HP), Université Paris Descartes, France |
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| Abstract: | Inductive learning algorithms have been suggested as alternatives to knowledge acquisition for expert systems. However, the application of machine learning algorithms often involves a number of subsidiary tasks to be performed as well as algorithm execution itself. It is important to help the domain expert manipulate his or her data so they are suitable for a specific algorithm, and subsequently to assess the algorithm results. These activities are often called preprocessing and postprocessing. This paper discusses issues related to the application of the ID3 algorithm, an important representative of the inductive learning family. A prototype workbench which has been developed to provide an integrated approach to the application of ID3 is presented. The design rationale and the potential use of the system is justified. Finally, future directions and further enhancements of the workbench are discussed. |
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