Open AccessPrivacy Protection and Perfect Classification Nature of C4.5 Algorithm
Author(s) -
K Chokkanathan,
S. Koteeswaran
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.24.12055
Subject(s) - homomorphic encryption , computer science , id3 algorithm , ambiguity , decision tree , id3 , algorithm , incremental decision tree , classifier (uml) , class (philosophy) , data mining , encryption , decision tree learning , node (physics) , artificial intelligence , theoretical computer science , machine learning , computer security , structural engineering , programming language , engineering
C4.5 algorithm is developed by Ross Quinlan which is the extension of ID3 algorithm used for generating a decision trees.Since the tree generated by C4.5 can be used for classification, so it’s also referred to as statistical classifier.Even though the Random Decision Tree is used to avoid the information leakage there are some problems and issues related to privacy maintenance.When we try to instantiate more instances for one class it leads to ambiguity at the same time creating new classes more and more will increase the complexity in RDT. These problems can be resolved by using our C4.5 algorithm.We can have any number of nodes in a network, each node can create its own tree or class and each class can initiate many number of instances for a disseminated classification consuming secure amount or threshold homomorphic encryption. The main objective of this paper is to discuss the ideal nature of the C4.5 algorithm and how they support this algorithm to be utilized in various datamining process.