Benchmark evaluation of classification methods for single label learning with R

Classification in data mining is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics items and based on a training set of previously labeled items. The objective of this paper is to introduce, explain and compare the performance of the single - labeled supervised learning algorithms in R language on benchmark single labeled data set. The traditional classification algorithms like Decision Tree, Naïve Bayes, Support Vector Machine, Random Forest, Classification and Regression Trees are used under inspection. The R language is chosen to see the classification performances. Four measures (sensitivity, specificity, accuracy, F - measure) of performance here considered are based on confusion matrix, table of counts revealing the performance of algorithm's confusion regarding the true classifications. The observation of all the four performance measures lead to infer that the Decision Tree outperforms than other classification methods.