A HYBRID CLASSIFICATION MODEL EMPLOYING GENETIC ALGORITHM AND ROOT GUIDED DECISION TREE FOR IMPROVED CATEGORIZATION OF DATA

Data mining algorithms play a major role in analyzing the vast data available in many fields like multimedia, medicine, business, education etc. Classification techniques have been extensively adopted for the purpose of pattern analysis. Several classification algorithms have been proposed in the li...

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Bibliographic Details
Published in:ARPN journal of engineering and applied sciences 2015-11, Vol.10 (21), p.9968-9975
Main Authors: Ramani, R Geetha, Balasubramanian, Lakshmi, Alaghu, Meenal A
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Data mining
Decision trees
Genetic algorithms
Roots
Searching
Online Access:Get full text
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Summary:Data mining algorithms play a major role in analyzing the vast data available in many fields like multimedia, medicine, business, education etc. Classification techniques have been extensively adopted for the purpose of pattern analysis. Several classification algorithms have been proposed in the literature. Yet demand exists for classification algorithms that yield higher accuracies. Hybrid classification procedures were also attempted in the literature. In this paper, the concept of Genetic Algorithm and Decision Tree is employed collectively for achieving better accuracies. The proposed methodology adopts genetic search to generate subsets of the attributes of the data and these subsets are evaluated using the Root Guided Decision Tree. This process results in a final decision tree with relevant set of attributes and yielding higher accuracy. The algorithm is validated on the datasets obtained from UCI repository and retinal dataset acquired from a publicly available High Resolution Fundus image Dataset.
ISSN:1819-6608
1819-6608