Ranjit Abraham

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As a probability-based statistical classification method, the Naïve Bayesian classifier has gained wide popularity despite its assumption that attributes are conditionally mutually independent given the class label. Improving the predictive accuracy and achieving dimensionality reduction for statistical classifiers has been an active research area in(More)
Naive Bayes classifier has gained wide popularity as a probability-based classification method despite its assumption that attributes are conditionally mutually independent given the class label. This paper makes a study into discretization techniques to improve the classification accuracy of Naive Bayes with respect to medical datasets. Our experimental(More)
Much research work in datamining has gone into improving the predictive accuracy of statistical classifiers by applying the techniques of discretization and feature selection. As a probability-based statistical classification method, the Naive Bayesian classifier has gained wide popularity despite its assumption that attributes are conditionally mutually(More)
Statistical classifiers typically build (parametric) probabilistic models of the training data, and compute the probability that an unknown sample belongs to each of the possible classes using these models. We utilize two established measures to compare the performance of statistical classifiers namely; classification accuracy (or error rate) and the area(More)
Medical diagnosis is considered as an important yet complicated task that needs to be executed accurately and efficiently. The automation of this system will be very useful for the medical field. Due to recent technology advances, large masses of medical data are available. These large data contain valuable information for diagnosing diseases. Text mining(More)
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