Fuzzy SLIQ Decision Tree for Quantitative Data-sets

Satya P Kumar Somayajula; Praveen Kumar Pentakota; Dr. C.P.V.N.J Mohan Rao

Fuzzy SLIQ Decision Tree for Quantitative Data-sets

Satya P Kumar Somayajula * §

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Praveen Kumar Pentakota

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Dr. C.P.V.N.J Mohan Rao

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§ Avanthi Institute Of Engineering & Technology, Visak...

Fuzzy SLIQ Decision Tree for Quantitative Data-sets

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Abstract

Decision trees are powerful and popular tools for classification and prediction in knowledge discovery and data mining this area gained that much importance because it enables modeling and knowledge extraction from the abundance of data available. Due to limitation of sharp decision boundaries decision tree algorithms are not that much effectively implemented to define real time classification problem. When the results are larger and deeper for a decision tree it leads to inexplicable induction rules which is another important parameter to be considered. In this paper we are proposing a fuzzy super vised learning in Quest decision tree (FS-DT) algorithm where we focused to design a fuzzy decision boundary instead of a crisp decision boundary. The SLIQ decision tree algorithm which is used to construct a fuzzy binary decision tree is modified here by FS-DT to reduce the size of the decision tree, using several real-life datasets taken from the UCI Machine Learning Repository performance of the FS-DT algorithm is compared with SLIQ .Several comparisons between SLIQ and FS-DT has been proposed in this paper which out comes the constraints of Traditional decision tree algorithms.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Satya Somayajula, Praveen Pentakota, Dr. Rao. 1970. "Fuzzy SLIQ Decision Tree for Quantitative Data-sets". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 19).

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GJCST Volume 11 Issue 19

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

algorithm binary fuzzy Repository SLIQ traditional

Classification

GJCST Classification F.4.3

F.4.1

Submission ReceivedAugust 25, 2011
Peer Review Double Blind
Handling Editor
Accepted September 24, 2011
Published October 9, 2011

Version of record

v1.2

Issue date

November 11, 2011

Language

English

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Article Matrices

Total Score: 148

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Satya P Kumar Somayajula, Praveen Kumar Pentakota, Dr. C.P.V.N.J Mohan Rao (PhD/Dr. count: 1)

View Count (all-time): 242

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Publish Date: 2011 10, Sun

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Total Views: 7.2K

Total Downloads: 483

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