An integration of cloud transform and rough set theory to induction of decision trees

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An integration of cloud transform and rough set theory to induction of decision trees. / Song, Jing; Li, Tianrui; Ruan, Da; Kabak, Özgür (Peer reviewer).

In: Fundamenta Informaticae, Vol. 94, No. 2, 11.2009, p. 261-273.

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Song, Jing ; Li, Tianrui ; Ruan, Da ; Kabak, Özgür. / An integration of cloud transform and rough set theory to induction of decision trees. In: Fundamenta Informaticae. 2009 ; Vol. 94, No. 2. pp. 261-273.

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@article{d6834f26cb264757842455641a1788d8,
title = "An integration of cloud transform and rough set theory to induction of decision trees",
abstract = "Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations. This paper presents a new algorithm, decision tree construction based on the Cloud transform and Rough set theory under the characteristic relation (CR), for mining classification knowledge from a given data set. The continuous data is transformed into discrete qualitative concepts via the cloud transformation and then the attribute with the smallest weighted mean roughness under the characteristic relation is selected as the current splitting node. Experimental evaluation shows the decision trees constructed by the CR algorithm tend to have a simpler structure, much higher classification accuracy and more understandable rules than those by C5.0 in most cases.",
keywords = "Rough set theory, cloud transform, decision trees, weighted mean roughness, characteristic relation",
author = "Jing Song and Tianrui Li and Da Ruan and {\"O}zg{\"u}r Kabak",
note = "Score = 10",
year = "2009",
month = "11",
doi = "10.3233/FI-2009-130",
language = "English",
volume = "94",
pages = "261--273",
journal = "Fundamenta Informaticae",
issn = "0169-2968",
publisher = "PTM - Polskie Towarzystwo Matematyczne",
number = "2",

}

RIS - Download

TY - JOUR

T1 - An integration of cloud transform and rough set theory to induction of decision trees

AU - Song, Jing

AU - Li, Tianrui

AU - Ruan, Da

A2 - Kabak, Özgür

N1 - Score = 10

PY - 2009/11

Y1 - 2009/11

N2 - Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations. This paper presents a new algorithm, decision tree construction based on the Cloud transform and Rough set theory under the characteristic relation (CR), for mining classification knowledge from a given data set. The continuous data is transformed into discrete qualitative concepts via the cloud transformation and then the attribute with the smallest weighted mean roughness under the characteristic relation is selected as the current splitting node. Experimental evaluation shows the decision trees constructed by the CR algorithm tend to have a simpler structure, much higher classification accuracy and more understandable rules than those by C5.0 in most cases.

AB - Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations. This paper presents a new algorithm, decision tree construction based on the Cloud transform and Rough set theory under the characteristic relation (CR), for mining classification knowledge from a given data set. The continuous data is transformed into discrete qualitative concepts via the cloud transformation and then the attribute with the smallest weighted mean roughness under the characteristic relation is selected as the current splitting node. Experimental evaluation shows the decision trees constructed by the CR algorithm tend to have a simpler structure, much higher classification accuracy and more understandable rules than those by C5.0 in most cases.

KW - Rough set theory

KW - cloud transform

KW - decision trees

KW - weighted mean roughness

KW - characteristic relation

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_100761

UR - http://knowledgecentre.sckcen.be/so2/bibref/6152

U2 - 10.3233/FI-2009-130

DO - 10.3233/FI-2009-130

M3 - Article

VL - 94

SP - 261

EP - 273

JO - Fundamenta Informaticae

JF - Fundamenta Informaticae

SN - 0169-2968

IS - 2

ER -

ID: 296186