Analyzing the ability of Naive-Bayes and Label Spreading to predict labels with varying quantities of training data : Classifier Evaluation
Abstract: A study was performed on Naive-Bayes and Label Spread- ing methods applied in a spam filter as classifiers. In the testing procedure their ability to predict was observed and the results were compared in a McNemar test; leading to the discovery of the strengths and weaknesses of the chosen methods in a environment of varying training data. Though the results were inconclusive due to resource restrictions, the theory is discussed from various angles in order to pro- vide a better understanding of the conditions that can lead to potentially different results between the chosen meth- ods; opening up for improvement and further studies. The conclusion made of this study is that a significant differ- ence exists in terms of ability to predict labels between the two classifiers. On a secondary note it is recommended to choose a classifier depending on available training data and computational power.
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