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Survey Paper on Implementation of ID3 Algorithm

Author(s):

Kalpana T Kanade , Everest College of Engineering,Aurangabad; Ashwini V Rajguru, Everest College of Engineering,Aurangabad

Keywords:

Data Mining, Decision Tree, ID3 Algorithm, Entropy, Information Gain, Example

Abstract

This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The example has several attributes and belongs to a class (like yes or no). The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. [1].

Other Details

Paper ID: IJSRDV4I50560
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 960-963

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