ROCCER: an Algorithm for Rule Learning Based on ROC Analysis

RC Prati; PA Flach

ROCCER: an Algorithm for Rule Learning Based on ROC Analysis

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

30 Citations (Scopus)

Abstract

We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules ? for instance found by Apriori ? using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose the rule set also have higher support and stronger association indexes.

Translated title of the contribution	ROCCER: an Algorithm for Rule Learning Based on ROC Analysis
Original language	English
Title of host publication	Unknown
Publisher	IJCAI
Pages	823 - 828
Number of pages	5
ISBN (Print)	0938075934
Publication status	Published - Aug 2005

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05)

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@inproceedings{718f41df69764750857ee1df7c49dab1,

title = "ROCCER: an Algorithm for Rule Learning Based on ROC Analysis",

abstract = " We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules ? for instance found by Apriori ? using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose the rule set also have higher support and stronger association indexes. ",

author = "RC Prati and PA Flach",

note = "Conference Proceedings/Title of Journal: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05)",

year = "2005",

month = aug,

language = "English",

isbn = "0938075934",

pages = "823 -- 828",

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publisher = "IJCAI",

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T1 - ROCCER: an Algorithm for Rule Learning Based on ROC Analysis

AU - Prati, RC

AU - Flach, PA

N1 - Conference Proceedings/Title of Journal: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05)

PY - 2005/8

Y1 - 2005/8

N2 - We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules ? for instance found by Apriori ? using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose the rule set also have higher support and stronger association indexes.

AB - We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules ? for instance found by Apriori ? using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose the rule set also have higher support and stronger association indexes.

M3 - Conference Contribution (Conference Proceeding)

SN - 0938075934

SP - 823

EP - 828

BT - Unknown

PB - IJCAI

ER -

ROCCER: an Algorithm for Rule Learning Based on ROC Analysis

Abstract

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