A Programming Paradigm for Machine Learning, with a Case Study of Bayesian Networks

Allison, L.

Inductive programming is a new machine learning paradigm which combines functional programming for writing statistical models and information theory to prevent overfitting. Type-classes specify general properties that models must have. Many statistical models, estimators and operators have polymorphic types. Useful operators combine models, and estimators, to form new ones; Functional programming compositional style of programming is a great advantage in this domain. Complementing this, information theory provides a compositional measure of the complexity of a model from its parts. Inductive programming is illustrated by a case study of Bayesian networks. Networks are built from classification - (decision-) trees. Trees are built from partitioning functions and models on data-spaces. Trees, and hence networks, are general as a natural consequence of the method. Discrete and continuous variables, and missing values are handled by the networks. Finally the Bayesian networks are applied to a challenging data set on lost persons.

Cite as: Allison, L. (2006). A Programming Paradigm for Machine Learning, with a Case Study of Bayesian Networks. In Proc. Twenty-Ninth Australasian Computer Science Conference (ACSC 2006), Hobart, Australia. CRPIT, 48. Estivill-Castro, V. and Dobbie, G., Eds. ACS. 103-111.

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