RSD: Relational Subgroup Discovery through First-Order Feature Construction

Lavrac Nada; Zelezny Filip; Peter Flach

RSD: Relational Subgroup Discovery through First-Order Feature Construction

Lavrac Nada, Zelezny Filip, Peter Flach

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Relational rule learning is typically used in solving classi- fication and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a proposition- alization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individual- centered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications applica- tion.

Translated title of the contribution	RSD: Relational Subgroup Discovery through First-Order Feature Construction
Original language	English
Title of host publication	Proceedings of the 12th International Conference on Inductive Logic Programming
Pages	149-165
Publication status	Published - 2002

Bibliographical note

ISBN: 3540005676
Publisher: Springer-Verlag
Name and Venue of Conference: Proceedings of the 12th International Conference on Inductive Logic Programming
Other identifier: 2000552

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@inproceedings{4d3474bb70804a0281fd3f71a73e981d,

title = "RSD: Relational Subgroup Discovery through First-Order Feature Construction",

abstract = " Relational rule learning is typically used in solving classi- fication and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a proposition- alization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individual- centered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications applica- tion. ",

author = "Lavrac Nada and Zelezny Filip and Peter Flach",

note = "ISBN: 3540005676 Publisher: Springer-Verlag Name and Venue of Conference: Proceedings of the 12th International Conference on Inductive Logic Programming Other identifier: 2000552",

year = "2002",

language = "English",

pages = "149--165",

booktitle = "Proceedings of the 12th International Conference on Inductive Logic Programming",

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AU - Nada, Lavrac

AU - Filip, Zelezny

AU - Flach, Peter

N1 - ISBN: 3540005676 Publisher: Springer-Verlag Name and Venue of Conference: Proceedings of the 12th International Conference on Inductive Logic Programming Other identifier: 2000552

PY - 2002

Y1 - 2002

N2 - Relational rule learning is typically used in solving classi- fication and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a proposition- alization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individual- centered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications applica- tion.

AB - Relational rule learning is typically used in solving classi- fication and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a proposition- alization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individual- centered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications applica- tion.

M3 - Conference Contribution (Conference Proceeding)

SP - 149

EP - 165

BT - Proceedings of the 12th International Conference on Inductive Logic Programming

ER -

RSD: Relational Subgroup Discovery through First-Order Feature Construction

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