Forest CERN: A New Decision Forest Building Technique

Md Nasim Adnan; Md Zahidul Islam

doi:10.1007/978-3-319-31753-3_25

Forest CERN: A New Decision Forest Building Technique

Md Nasim Adnan, Md Zahidul Islam

Research output: Book chapter/Published conference paper › Conference paper › peer-review

28 Citations (Scopus)

6 Downloads (Pure)

Abstract

Persistent efforts are going on to propose more accurate decision forest building techniques. In this paper, we propose a new decision forest building technique called “Forest by Continuously Excluding Root Node (Forest CERN)”. The key feature of the proposed technique is that it strives to exclude attributes that participated in the root nodes of previous trees by imposing penalties on them to obstruct them appear in some subsequent trees. Penalties are gradually lifted in such a manner that those attributes can reappear after a while. Other than that, our technique uses bootstrap samples to generate predefined number of trees. The target of the proposed algorithm is to maximize tree diversity without impeding individual tree accuracy. We present an elaborate experimental results involving fifteen widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in most of the cases.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	20th Pacific-Asia Conference, PAKDD 2016
Editors	R Wang, J Bailey, T Washio, J Z Huang, L Khan, G Dobbie
Place of Publication	Switzerland
Publisher	Springer
Pages	304-315
Number of pages	12
Volume	9651
ISBN (Electronic)	9783319317533
ISBN (Print)	9783319317526
DOIs	https://doi.org/10.1007/978-3-319-31753-3_25
Publication status	Published - 2016
Event	The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016 - The University of Auckland, Auckland, New Zealand Duration: 19 Apr 2016 → 22 Apr 2016 http://pakdd2016.pakdd.org/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag
Volume	9651
ISSN (Print)	0302-9743

Conference

Conference	The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016
Country/Territory	New Zealand
City	Auckland
Period	19/04/16 → 22/04/16
Other	The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems and the emerging applications.
Internet address	http://pakdd2016.pakdd.org/

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10.1007/978-3-319-31753-3_25

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Adnan, M. N., & Islam, M. Z. (2016). Forest CERN: A New Decision Forest Building Technique. In R. Wang, J. Bailey, T. Washio, J. Z. Huang, L. Khan, & G. Dobbie (Eds.), Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016 (Vol. 9651, pp. 304-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9651). Springer. https://doi.org/10.1007/978-3-319-31753-3_25

Adnan, Md Nasim ; Islam, Md Zahidul. / Forest CERN : A New Decision Forest Building Technique. Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. editor / R Wang ; J Bailey ; T Washio ; J Z Huang ; L Khan ; G Dobbie. Vol. 9651 Switzerland : Springer, 2016. pp. 304-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Persistent efforts are going on to propose more accurate decision forest building techniques. In this paper, we propose a new decision forest building technique called “Forest by Continuously Excluding Root Node (Forest CERN)”. The key feature of the proposed technique is that it strives to exclude attributes that participated in the root nodes of previous trees by imposing penalties on them to obstruct them appear in some subsequent trees. Penalties are gradually lifted in such a manner that those attributes can reappear after a while. Other than that, our technique uses bootstrap samples to generate predefined number of trees. The target of the proposed algorithm is to maximize tree diversity without impeding individual tree accuracy. We present an elaborate experimental results involving fifteen widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in most of the cases.",

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doi = "10.1007/978-3-319-31753-3_25",

language = "English",

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Adnan, MN & Islam, MZ 2016, Forest CERN: A New Decision Forest Building Technique. in R Wang, J Bailey, T Washio, JZ Huang, L Khan & G Dobbie (eds), Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. vol. 9651, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9651, Springer, Switzerland, pp. 304-315, The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016, Auckland, New Zealand, 19/04/16. https://doi.org/10.1007/978-3-319-31753-3_25

Forest CERN: A New Decision Forest Building Technique. / Adnan, Md Nasim; Islam, Md Zahidul.
Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. ed. / R Wang; J Bailey; T Washio; J Z Huang; L Khan; G Dobbie. Vol. 9651 Switzerland: Springer, 2016. p. 304-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9651).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Forest CERN

T2 - The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016

AU - Adnan, Md Nasim

AU - Islam, Md Zahidul

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = Springer International Publishing Switzerland 2016, 2016. Event dates (773o) = 19-22 April 2016; Parent title (773t) = Pacific Asia Conference on Knowledge Discovery and Data Mining.

PY - 2016

Y1 - 2016

N2 - Persistent efforts are going on to propose more accurate decision forest building techniques. In this paper, we propose a new decision forest building technique called “Forest by Continuously Excluding Root Node (Forest CERN)”. The key feature of the proposed technique is that it strives to exclude attributes that participated in the root nodes of previous trees by imposing penalties on them to obstruct them appear in some subsequent trees. Penalties are gradually lifted in such a manner that those attributes can reappear after a while. Other than that, our technique uses bootstrap samples to generate predefined number of trees. The target of the proposed algorithm is to maximize tree diversity without impeding individual tree accuracy. We present an elaborate experimental results involving fifteen widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in most of the cases.

AB - Persistent efforts are going on to propose more accurate decision forest building techniques. In this paper, we propose a new decision forest building technique called “Forest by Continuously Excluding Root Node (Forest CERN)”. The key feature of the proposed technique is that it strives to exclude attributes that participated in the root nodes of previous trees by imposing penalties on them to obstruct them appear in some subsequent trees. Penalties are gradually lifted in such a manner that those attributes can reappear after a while. Other than that, our technique uses bootstrap samples to generate predefined number of trees. The target of the proposed algorithm is to maximize tree diversity without impeding individual tree accuracy. We present an elaborate experimental results involving fifteen widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in most of the cases.

U2 - 10.1007/978-3-319-31753-3_25

DO - 10.1007/978-3-319-31753-3_25

M3 - Conference paper

SN - 9783319317526

VL - 9651

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Knowledge Discovery and Data Mining

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A2 - Bailey, J

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A2 - Dobbie, G

PB - Springer

CY - Switzerland

Y2 - 19 April 2016 through 22 April 2016

ER -

Adnan MN, Islam MZ. Forest CERN: A New Decision Forest Building Technique. In Wang R, Bailey J, Washio T, Huang JZ, Khan L, Dobbie G, editors, Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. Vol. 9651. Switzerland: Springer. 2016. p. 304-315. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-31753-3_25

Forest CERN: A New Decision Forest Building Technique

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