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A Novel Approach for the Assessment of Decision Stump & Upgraded Rf Classification Algorithms


Affiliations
1 School of Computer Science and Applications, REVA University, Bangalore, Karnataka, India
2 Department of Computer Science, Sri Venkateswara University, Tirupati, Andhra Pradesh,, India
 

The classification models in data mining consists of decision tree, neural network, genetic algorithm, rough set, statistical model, etc. In this research, we have proposed and deliberated a new algorithm called Upgraded Random Forest, which is applied for the classification of sensor discrimination dataset. Here we considered for classification of multisource Sensor Discrimination data. The Upgraded RF approach becomes extreme attention for multi-source classification. The methodology which we are developed is not only a nonparametric but it also applies for the assessment and significance of the specific variables in the classification.

Keywords

Data Mining, Classification, Decision Stump, Random Forest and Upgraded RF.
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  • A Novel Approach for the Assessment of Decision Stump & Upgraded Rf Classification Algorithms

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Authors

M. Jayakameswaraiah
School of Computer Science and Applications, REVA University, Bangalore, Karnataka, India
R. Pinakapani
School of Computer Science and Applications, REVA University, Bangalore, Karnataka, India
Ravi Dandu
School of Computer Science and Applications, REVA University, Bangalore, Karnataka, India
S. Ramakrishna
Department of Computer Science, Sri Venkateswara University, Tirupati, Andhra Pradesh,, India

Abstract


The classification models in data mining consists of decision tree, neural network, genetic algorithm, rough set, statistical model, etc. In this research, we have proposed and deliberated a new algorithm called Upgraded Random Forest, which is applied for the classification of sensor discrimination dataset. Here we considered for classification of multisource Sensor Discrimination data. The Upgraded RF approach becomes extreme attention for multi-source classification. The methodology which we are developed is not only a nonparametric but it also applies for the assessment and significance of the specific variables in the classification.

Keywords


Data Mining, Classification, Decision Stump, Random Forest and Upgraded RF.

References