A Novel Ensemble Based Decision Tree Model For High Dimensional Biomedicine Data

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2016 by IJCOT Journal
Volume - 6 Issue - 6
Year of Publication : 2016
AuthorsDande Rushalini, Mr. K. Vijay Kumar
  10.14445/22492593/IJCOT-V37P305

MLA

Dande Rushalini, Mr. K. Vijay Kumar"A Novel Ensemble Based Decision Tree Model For High Dimensional Biomedicine Data", International Journal of Computer & organization Trends (IJCOT), V6(6):20-24 Nov - Dec 2016, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract Knowledge discovery is an essantial mechanism for the intelligent data analysis to transform data in to meaningful information that will support for the decision making data mining approaches support automatic extraction of data and attempts to discover the hidden rules and patterns in data and also detect relevant decision rules from the high dimensional dataset. Classification from imbalanced data is significantly affected the performance of the algorithm due to noise and high dimensionality. Sparsity and high dimensionality of the classifier algorithm becomes a major problem in many traditional decision tree models on medical datasets. A novel decision trees allows estimating on topmost features to assess the class prior probability and estimates the chance of misleading false positive patterns. In this research work, a new framework is proposed by integrating random forest decision tree for pattern analysis. Experimental results show that proposed model has better accuracy compare to existing approaches.

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Keywords-
PSO model, Disease detection, Random forest, classification ,UCI repository.