Proactive data mining using decision trees

Haim Dahan; Oded Maimon; Shahar Cohen; Lior Rokach

doi:10.1109/EEEI.2012.6377048

Proactive data mining using decision trees

Haim Dahan
, Oded Maimon
, Shahar Cohen
, Lior Rokach

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Most of the existing data mining algorithms are 'passive'. That is, they produce models which can describe patterns, but leave the decision on how to react to these patterns in the hands of the user. In contrast, in this work we describe a proactive approach to data mining, and describe an implementation of that approach, using decision trees. We show that the proactive role requires the algorithms to consider additional domain knowledge, which is exogenous to the training set. We also suggest a novel splitting criterion, termed maximalutility, which is driven by the proactive agenda.

Original language	English
Title of host publication	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
DOIs	https://doi.org/10.1109/EEEI.2012.6377048
State	Published - 1 Dec 2012
Event	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 - Eilat, Israel Duration: 14 Nov 2012 → 17 Nov 2012

Publication series

Name	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012

Conference

Conference	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
Country/Territory	Israel
City	Eilat
Period	14/11/12 → 17/11/12

Keywords

Active Data Mining
Classification
Knowledge Discovery from Databases

ASJC Scopus subject areas

Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EEEI.2012.6377048

Cite this

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title = "Proactive data mining using decision trees",

abstract = "Most of the existing data mining algorithms are 'passive'. That is, they produce models which can describe patterns, but leave the decision on how to react to these patterns in the hands of the user. In contrast, in this work we describe a proactive approach to data mining, and describe an implementation of that approach, using decision trees. We show that the proactive role requires the algorithms to consider additional domain knowledge, which is exogenous to the training set. We also suggest a novel splitting criterion, termed maximalutility, which is driven by the proactive agenda.",

keywords = "Active Data Mining, Classification, Knowledge Discovery from Databases",

author = "Haim Dahan and Oded Maimon and Shahar Cohen and Lior Rokach",

year = "2012",

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doi = "10.1109/EEEI.2012.6377048",

language = "English",

isbn = "9781467346801",

series = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

booktitle = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

note = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 ; Conference date: 14-11-2012 Through 17-11-2012",

}

Dahan, H, Maimon, O, Cohen, S & Rokach, L 2012, Proactive data mining using decision trees. in 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012., 6377048, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, Eilat, Israel, 14/11/12. https://doi.org/10.1109/EEEI.2012.6377048

Proactive data mining using decision trees. / Dahan, Haim; Maimon, Oded; Cohen, Shahar et al.
2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012. 2012. 6377048 (2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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N2 - Most of the existing data mining algorithms are 'passive'. That is, they produce models which can describe patterns, but leave the decision on how to react to these patterns in the hands of the user. In contrast, in this work we describe a proactive approach to data mining, and describe an implementation of that approach, using decision trees. We show that the proactive role requires the algorithms to consider additional domain knowledge, which is exogenous to the training set. We also suggest a novel splitting criterion, termed maximalutility, which is driven by the proactive agenda.

AB - Most of the existing data mining algorithms are 'passive'. That is, they produce models which can describe patterns, but leave the decision on how to react to these patterns in the hands of the user. In contrast, in this work we describe a proactive approach to data mining, and describe an implementation of that approach, using decision trees. We show that the proactive role requires the algorithms to consider additional domain knowledge, which is exogenous to the training set. We also suggest a novel splitting criterion, termed maximalutility, which is driven by the proactive agenda.

KW - Active Data Mining

KW - Classification

KW - Knowledge Discovery from Databases

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DO - 10.1109/EEEI.2012.6377048

M3 - Conference contribution

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Proactive data mining using decision trees

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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