Performance Analysis of K-NN and Naïve Bayes Classifiers for Spam Filtering Application

Pattern classification is one of the most important and leading aspects of modern image processing systems. By training a classifier on a set of data, the unseen samples can be categorized as much accurate as training has been done. There are many different classifiers having varying accuracies, des...

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Bibliographic Details
Published in:International journal of advanced research in computer science 2011-03, Vol.2 (2)
Main Authors: Iqbal, M Aqeel, Shoukat, Arslan, Khan, Shoaib A, Iqbal, Saleem
Format: Article
Language:English
Subjects:
Algorithms
Classification
Computer science
Datasets
Learning
Mechanical engineering
Teachers
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Summary:Pattern classification is one of the most important and leading aspects of modern image processing systems. By training a classifier on a set of data, the unseen samples can be categorized as much accurate as training has been done. There are many different classifiers having varying accuracies, design complexities and performance. With different design strategies these classifiers may have different characteristics. In this paper a performance analysis of K-NN and Naïve Bayes classifiers have been presented for the classification of spam emails. Different design aspects of both classifiers have also been presented in terms of computational complexity and classification accuracy against their performance.
ISSN:0976-5697