Type Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we present a learning algorithm for Type Extension Trees (TET) that discovers informative count-of-count features in the supervised learning setting. Experiments on bibliographic data show that TET-learning is able to discover the count-of-count feature underlying the definition of the h-index, and the inverse document frequency feature commonly used in information retrieval. We also introduce a metric on TET feature values. This metric is defined as a recursive application of the Wasserstein-Kantorovich metric. Experiments with a k-NN classifier show that exploiting the recursive count-of-count statistics encoded in TET values improves classification accuracy over alternative methods based on simple count statistics. © 2013 Elsevier B.V.

Type Extension Trees for feature construction and learning in relational domains / Jaeger, Manfred; Lippi, Marco; Passerini, Andrea; Frasconi, Paolo. - In: ARTIFICIAL INTELLIGENCE. - ISSN 0004-3702. - 204:(2013), pp. 30-55. [10.1016/j.artint.2013.08.002]

Type Extension Trees for feature construction and learning in relational domains

LIPPI, MARCO;
2013

Abstract

Type Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we present a learning algorithm for Type Extension Trees (TET) that discovers informative count-of-count features in the supervised learning setting. Experiments on bibliographic data show that TET-learning is able to discover the count-of-count feature underlying the definition of the h-index, and the inverse document frequency feature commonly used in information retrieval. We also introduce a metric on TET feature values. This metric is defined as a recursive application of the Wasserstein-Kantorovich metric. Experiments with a k-NN classifier show that exploiting the recursive count-of-count statistics encoded in TET values improves classification accuracy over alternative methods based on simple count statistics. © 2013 Elsevier B.V.
2013
204
30
55
Type Extension Trees for feature construction and learning in relational domains / Jaeger, Manfred; Lippi, Marco; Passerini, Andrea; Frasconi, Paolo. - In: ARTIFICIAL INTELLIGENCE. - ISSN 0004-3702. - 204:(2013), pp. 30-55. [10.1016/j.artint.2013.08.002]
Jaeger, Manfred; Lippi, Marco; Passerini, Andrea; Frasconi, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1122680
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