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Prediction of Online Vehicle Insurance System using Bayes Classifier – A Proposed Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 49 - Number 16 |
| Year of Publication: 2012 |
| Authors: S. S. Thakur, J. K. Sing |
10.5120/7714-1130
|
S. S. Thakur, J. K. Sing . Prediction of Online Vehicle Insurance System using Bayes Classifier – A Proposed Approach. International Journal of Computer Applications. 49, 16 ( July 2012), 38-42. DOI=10.5120/7714-1130
Abstract
A classification technique (or classifier) is a systematic approach used in building classification models from an input data set. Some examples include decision tree classifier, rule based classifiers, neural networks, support vector machines and naïve Bayes classifiers. Each technique employs a learning algorithm to identify a model that best fits the relationships between the attribute set and the class label of the input data. The model generated by the learning algorithm should both fit 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 that accurately predict the class labels of previously unknown records. In this work prediction has been done for the entire test set data, and accuracy is found to be good, compared to the relative performance of different classifiers namely decision tree classifier.
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