An Exploratory Analysis between the Feature Selection Algorithms IGMBD and IGChiMerge
Автор: P.Kalpana, K.Mani
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 7 Vol. 9, 2017 года.
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Most of the data mining and machine learning algorithms will work better with discrete data rather than continuous. But the real time data need not be always discrete and thus it is necessary to discretize the continuous features. There are several discretization methods available in the literature. This paper compares the two methods Median Based Discretization and ChiMerge discretization. The discretized values obtained using both methods are used to find the feature relevance using Information Gain. Using the feature relevance, the original features are ranked by both methods and the top ranked attributes are selected as the more relevant ones. The selected attributes are then fed into the Naive Bayesian Classifier to determine the predictive accuracy. The experimental results clearly show that the performance of the Naive Bayesian Classifier has improved significantly for the features selected using Information Gain with Median Based Discretization than Information Gain with ChiMerge discretization.
ChiMerge Discretization, Feature Selection, Median Based Discretization, Naive Bayesian Classifier, Predictive Accuracy and Relevant Features
Короткий адрес: https://sciup.org/15012665
IDR: 15012665
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