Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method

Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual problem.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages305-310
Number of pages6
Publication statusPublished - 2006
Event6th IEEE International Conference on Data Mining - Workshops, ICDM 2006 - Hong Kong, China
Duration: 2006 Dec 182006 Dec 18

Other

Other6th IEEE International Conference on Data Mining - Workshops, ICDM 2006
CountryChina
CityHong Kong
Period06/12/1806/12/18

Fingerprint

Learning algorithms
Data mining
Processing
Learning systems
Costs

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Abe, H., Tsumoto, S., Ohsaki, M., & Yamaguchi, T. (2006). Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 305-310). [4063644]

Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method. / Abe, Hidenao; Tsumoto, Shusaku; Ohsaki, Miho; Yamaguchi, Takahira.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 305-310 4063644.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abe, H, Tsumoto, S, Ohsaki, M & Yamaguchi, T 2006, Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method. in Proceedings - IEEE International Conference on Data Mining, ICDM., 4063644, pp. 305-310, 6th IEEE International Conference on Data Mining - Workshops, ICDM 2006, Hong Kong, China, 06/12/18.
Abe H, Tsumoto S, Ohsaki M, Yamaguchi T. Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 305-310. 4063644
Abe, Hidenao ; Tsumoto, Shusaku ; Ohsaki, Miho ; Yamaguchi, Takahira. / Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method. Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. pp. 305-310
@inproceedings{b398104709e8444da0f25b4171dfdfa2,
title = "Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method",
abstract = "In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual problem.",
author = "Hidenao Abe and Shusaku Tsumoto and Miho Ohsaki and Takahira Yamaguchi",
year = "2006",
language = "English",
isbn = "0769527027",
pages = "305--310",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",

}

TY - GEN

T1 - Evaluating learning algorithms composed by a constructive meta-learning scheme for a rule evaluation support method

AU - Abe, Hidenao

AU - Tsumoto, Shusaku

AU - Ohsaki, Miho

AU - Yamaguchi, Takahira

PY - 2006

Y1 - 2006

N2 - In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual problem.

AB - In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual problem.

UR - http://www.scopus.com/inward/record.url?scp=78449301983&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78449301983&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78449301983

SN - 0769527027

SN - 9780769527024

SP - 305

EP - 310

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

ER -