Performance evaluation of supervised learning algorithms with various training data sizes and missing attributes

Abstract

Supervised learning is a machine learning technique used for creating a data prediction model. This article focuses on finding high performance supervised learning algorithms with varied training data sizes, varied number of attributes, and time spent on prediction. This studied evaluated seven algorithms, Boosting, Random Forest, Bagging, Naive Bayes, K-Nearest Neighbours (K-NN), Decision Tree, and Support Vector Machine (SVM), on seven data sets that are the standard benchmark from University of California, Irvine (UCI) with two evaluation metrics and experimental settings of various training data sizes and missing key attributes. Our findings reveal that Bagging, Random Forest, and SVM are overall the three most accurate algorithms. However, when presence of key attribute values is of concern, K-NN is recommended as its performance is affected the least. Alternatively, when training data sizes may be not large enough, Naive Bayes is preferable since it is the most insensitive algorithm to training data sizes. The algorithms are characterized on a two-dimension chart based on prediction performance and computation time. This chart is expected to guide a novice user to choose an appropriate method for his/her demand. Based on this chart, in general, Bagging and Random Forest are the two most recommended algorithms because of their high performance and speed.