Constructing Relational Features from Background Knowledge for Inductive Machine Learning
نویسنده
چکیده
Most existing inductive learning systems form concept descriptions in propositional languages from vectors of basic features. However, many concepts are characterized by the relationships of individual examples to general domain knowledge. We describe a system that constructs relational terms e ciently to augment the description language of standard inductive systems. In our approach, examples and domain knowledge are combined into an inheritance network, and a form of spreading activation is used to nd relevant relational terms. Since there is an equivalence between inheritance networks and relational databases, this yields a method for exploring tables in the database and nding relevant relationships among data to characterize concepts. We also describe the implementation of a prototype system on the CM-2 parallel computer and some experiments with large data sets.
منابع مشابه
Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning
Most existing inductive learning systems form concept descriptions in propositional languages from vectors of basic features. However, many concepts are characterized by the relationships of individual examples to general domain knowledge. We describe a system that constructs relational terms e~ciently to augment the description language of standard inductive systems. In our approach, examples ...
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