Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 103
Country: India
Subject: Global Journal of Research in Engineering - I: Numerical Methods
Authors: N.Madhuri,T.Nagalakshmi,D.Sujatha (PhD/Dr. count: 0)
View Count (all-time): 193
Total Views (Real + Logic): 5535
Total Downloads (simulated): 2664
Publish Date: 2011 12, Wed
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Neural Networks and Rules-based Systems used to Find Rational and
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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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