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Título: Machine-learning assisted development of a knowledge-based system in dairy farming
Autores: Pietersma, Diederik.
Fecha: 2001
Publicador: McGill University - MCGILL
Fuente:
Tipo: Electronic Thesis or Dissertation
Tema: Dairy farming -- Management.
Dairy farming -- Québec (Province) -- Management.
Machine learning.
Descripción: The goal of this research was to explore the use of machine learning to assist in the development of knowledge-based systems (KBS) in dairy farming. A framework was first developed which described the various types of management and control activities in dairy farming and the types of information flows among these activities. This framework provided a basis for the creation of computerized information systems and helped to identify the analysis of group-average lactation curves as a promising area of application. A case-acquisition and decision-support system was developed to assist a domain specialist in generating example cases for machine learning. The specialist classified data from 33 herds enrolled with the Quebec dairy herd analysis service, resulting in 1428 lactations and 7684 tests of individual cows, classified as outlier or non-outlier, and 99 interpretations of group-average lactation curves. To enable the performance analysis of classifiers, generated with machine learning from these small data sets, a method was established involving cross-validation runs, relative operating characteristic curves, and analysis of variance. In experiments to filter lactations and tests, classification performance was significantly affected by preprocessing of examples, creation of additional attributes, choice of machine-learning algorithm, and algorithm configuration. For the filtering of individual tests, naive-Bayes classification showed significantly better performance than decision-tree induction. However, the specialist considered the decision trees as more transparent than the knowledge generated with naive Bayes. The creation of a series of three classifiers with increased sensitivity at the expense of reduced specificity per classification task, allows users of a final KBS to choose the desired tendency of classifying new cases as abnormal. For the main interpretation tasks, satisfactory performance was achieved. For the filtering tasks, performance was fai
Idioma: en