Analysis of Data Mining Classification with Decision Tree Technique

1
Jully Samota
Jully Samota
2
Dharm Singh
Dharm Singh
3
Naveen Choudhary
Naveen Choudhary
1 Maharana Pratap University of Agriculture and Technology

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The diversity and applicability of data mining are increasing day to day so need to extract hidden patterns from massive data. The paper states the problem of attribute bias. Decision tree technique based on information of attribute is biased toward multi value attributes which have more but insignificant information content. Attributes that have additional values can be less important for various applications of decision tree. Problem affects the accuracy of ID3 Classifier and generate unclassified region. The performance of ID3 classification and cascaded model of RBF network for ID3 classification is presented here. The performance of hybrid technique ID3 with CRBF for classification is proposed. As shown through the experimental results ID3 classifier with CRBF accuracy is higher than ID3 classifier.

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.

Jully Samota. 2014. \u201cAnalysis of Data Mining Classification with Decision Tree Technique\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C13): .

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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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January 26, 2014

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The diversity and applicability of data mining are increasing day to day so need to extract hidden patterns from massive data. The paper states the problem of attribute bias. Decision tree technique based on information of attribute is biased toward multi value attributes which have more but insignificant information content. Attributes that have additional values can be less important for various applications of decision tree. Problem affects the accuracy of ID3 Classifier and generate unclassified region. The performance of ID3 classification and cascaded model of RBF network for ID3 classification is presented here. The performance of hybrid technique ID3 with CRBF for classification is proposed. As shown through the experimental results ID3 classifier with CRBF accuracy is higher than ID3 classifier.

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Analysis of Data Mining Classification with Decision Tree Technique

Dharm Singh
Dharm Singh
Naveen Choudhary
Naveen Choudhary
Jully Samota
Jully Samota Maharana Pratap University of Agriculture and Technology

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