Structural Stefan Kramer
نویسنده
چکیده
In many real-world domains the task of machine learning algorithms is to learn a theory for predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with nondeterminate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems. SRT integrates the statistical method of regression trees into ILP. It constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.
منابع مشابه
Representing and utilizing DDI in relational databases
Alerk Amin, CentERdata [[email protected]] Ingo Barkow, DIPF Leibniz Institute for Educational Research and Educational Information [[email protected]] Stefan Kramer, Cornell Institute for Social and Economic Research [[email protected]] David Schiller, Research Data Center (FDZ) of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB) [[email protected]]...
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