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Reseach Article

Prediction of Online Vehicle Insurance System using Decision Tree Classifier and Baye�s Classifier � A comparative Analysis

Published on October 2014 by S. S. Thakur, J. K. Sing
International Conference on Microelectronics, Circuits and Systems
Foundation of Computer Science USA
MICRO - Number 1
October 2014
Authors: S. S. Thakur, J. K. Sing
166c7d7d-d271-43a6-9209-6db9d662d441

S. S. Thakur, J. K. Sing . Prediction of Online Vehicle Insurance System using Decision Tree Classifier and Baye�s Classifier � A comparative Analysis. International Conference on Microelectronics, Circuits and Systems. MICRO, 1 (October 2014), 10-17.

@article{
author = { S. S. Thakur, J. K. Sing },
title = { Prediction of Online Vehicle Insurance System using Decision Tree Classifier and Baye�s Classifier � A comparative Analysis },
journal = { International Conference on Microelectronics, Circuits and Systems },
issue_date = { October 2014 },
volume = { MICRO },
number = { 1 },
month = { October },
year = { 2014 },
issn = 0975-8887,
pages = { 10-17 },
numpages = 8,
url = { /proceedings/micro/number1/18308-1804/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Microelectronics, Circuits and Systems
%A S. S. Thakur
%A J. K. Sing
%T Prediction of Online Vehicle Insurance System using Decision Tree Classifier and Baye�s Classifier � A comparative Analysis
%J International Conference on Microelectronics, Circuits and Systems
%@ 0975-8887
%V MICRO
%N 1
%P 10-17
%D 2014
%I International Journal of Computer Applications
Abstract

A classification technique (or classifier) is a systematic approach used in building classification models from an input data set. The model generated by the learning algorithm should fit both the input data well and correctly predict the class labels of records it has never seen before. Therefore, a key objective of the learning algorithm is to build models with good generalization capability i. e. models that accurately predict the class labels of previously unknown records. The accuracy or error rate computed from the test set can also be used to compare the relative performance of different classifiers on the same domain. However, the results obtained for accuracy is good and average error rate obtained is equally acceptable for the test records in which the class labels of the test records was not known , in both the classifiers. As computational complexity is concerned Bayes Classifier performs better than Decision Tree Classifier in our system. While the Decision Tree Classifier performed better than Bayes Classifier based on prediction in this system.

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Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Classifier Bayes Classifier Web Services Conditional Probability Posterior Probability Online Insurance Etc