Analyzing the ability of Naive-Bayes and Label Spreading to predict labels with varying quantities of training data
Classifier Evaluation, Robin Kammerlander, Tedy Warsitha · 2016
A study was performed on Naive-Bayes and Label Spreading 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 provide a better understanding of the conditions that can lead to potentially different results between the chosen methods; opening up for improvement and further studies. The conclusion made of this study is that a significant difference 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.