Comparing Supervised Machine Learning Algorithms on Classification Efficiency of multiclass classifications problem

Abstract

Multi-class classification is a fascinating field to study. However, evaluating the classification performance of classifiers is difficult. Class indices such as accuracy, precision, recall, and F-measure, Kappa and area under the curve of receiver operating characteristics (AUC), can be used to evaluate classification performance. These indices describe the classification results achieved on each modelled class. Several measures have been introduced in the literature to deal with this assessment, the most commonly used being accuracy. In general these metrics were proposed to address binary classification tasks, whereas multiclass classification is the more difficult and currently active research area in machine learning (ML). In this paper, we intended to compare classification performance of nine supervised machine learning algorithms based on three learner types: statistical learner, rule-based learner and neural-base learner by considering accuracy, precision, recall and F-measure and ROC area achieved on four different datasets from UCI machine repository. Among these, Random forest has been the best performance in both 10 fold cross validation and percentage split with overall average accuracy of predictive power of 92.20% and 91.76% respectively, with less variability, whereas Naïve Bayes has the worst also in both 10 fold cross validation and percentage split by average correct classification performance of 79.18% and 76.92% respectively, and also with higher variability next to Decision Table.