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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27768
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| Title: | Aggregation-Based Feature Invention and Relational Concept Classes |
| Authors: | Perlich, Claudia
Provost, Foster |
| Keywords: | Relational learning
aggregation
Feature Construction
Constructive induction
Propositionalization |
| Issue Date: | 24-Aug-2003 |
| Publisher: | SIGKDD |
| Citation: | In Proceedings of the Ninth SIGKDD International Conference on Knowledge
Discovery and Data Mining (KDD-2003). |
| Series/Report no.: | CeDER-PP-2003-03 |
| Abstract: | Model induction from relational data requires aggregation of values of
attributes of related entities. This paper makes three contributions to
the study of relational learning.(1) It presents a hierarchy of
relational concepts of increasing complexity, using relational schema
characteristics such as cardinality, and derives classes of aggregation
operators that are needed to learn these concepts. (2) Expanding one
level of the hierarchy, it introduces new aggregation operators that
model the distribution of the values to be aggregated and (for
classification problems) the differences in these distributions by
class. (3) It demonstrates empirically on a noisy business domain that
more-complex aggregation methods can increase generalization
performance. Constructing features using target-dependent aggregations
can transform relational prediction tasks so that well-understood
feature-vector-based modeling algorithms can be applied successfully. |
| URI: | http://hdl.handle.net/2451/27768 |
| Appears in Collections: | CeDER Published Papers
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