Decision Tree Construction: A continues label support Degree based Approach

1
N.Madhuri
N.Madhuri
2
T.Nagalakshmi
T.Nagalakshmi
3
D.Sujatha
D.Sujatha
1 Auroras Technological and Research Institute (JNTU)

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Data mining and classification systems utilize decision tree algorithms since they proffer rapid speediness, advanced exactness and also simple organization of those algorithms. An ideal decision can be built only when the appropriate attributes are chosen. This paper focuses on throwing light on choosing characteristics based on the theory of attribute support degree on account of which a unique decision tree construction algorithm is proposed on the basis of rough set and granular computing theory. It is henceforth proved that the decision tree proposed by the new approach yields far more better results in terms of precision and consistency as compared to the decision trees yielded by ID3, C4.5 and DTBAS.

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References

  1. J Quinlan (1986). Induction of Decision Trees.
  2. J Quinlan (1996). Improved Use of Continuous Attributes in C4.5.
  3. Farid Seifi,Mohammad Kangavari,Hamed Ahmadi,Ehsan Lotfi,Sanaz Imaniyan,Somayeh Lagzian (1982). Optimizing twins decision tree classification, using genetic algorithms.
  4. T Lin (2006). Granular Computing II Infrastructure for AI-Engineering Examples, Intuitions and Modeling.
  5. J Han,M Kamber (2001). Data Mining Concepts and Techniques.
  6. Baoshi Ding,Yongqing Zheng,Shaoyu Zang (2009). A New Decision Tree Algorithm Based on Rough Set Theory.
  7. D Miao,G Wang (2007). Granular Computing: Past, Now, Future[M).
  8. E Frank,M Hall WEKA.
  9. Qing Lin,; Zongzhuan Ding,Jianping Yong,Jun Zhou (2010). An algorithm of decision tree construction based on attribute support degree.
  10. Y Chen,C Hsu,S Chou (2003). Constructing a Multi-Valued and Multi-Labeled Decision Tree.

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.

N.Madhuri. 2011. \u201cDecision Tree Construction: A continues label support Degree based Approach\u201d. Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 11 (GJRE Volume 11 Issue I7): .

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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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December 28, 2011

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English

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Data mining and classification systems utilize decision tree algorithms since they proffer rapid speediness, advanced exactness and also simple organization of those algorithms. An ideal decision can be built only when the appropriate attributes are chosen. This paper focuses on throwing light on choosing characteristics based on the theory of attribute support degree on account of which a unique decision tree construction algorithm is proposed on the basis of rough set and granular computing theory. It is henceforth proved that the decision tree proposed by the new approach yields far more better results in terms of precision and consistency as compared to the decision trees yielded by ID3, C4.5 and DTBAS.

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Decision Tree Construction: A continues label support Degree based Approach

N.Madhuri
N.Madhuri Auroras Technological and Research Institute (JNTU)
T.Nagalakshmi
T.Nagalakshmi
D.Sujatha
D.Sujatha

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