Ensemble Learning Based on Soft Voting for Detecting Methamphetamine in Urine

Abstract

Recently, as the rapid progress of information and communication technology, robot technology, and artificial intelligence, we have become to build a higher level of safe, comfortable, and smart society coexisting with advanced technology. Methamphetamine addiction has become a major human social problem in the world. Traditional approaches of detecting methamphetamine through hair, skin, urine, and blood fluid are financially inefficient, time-consuming, and in some cases too complicated. Providing reliable and trustworthy detection with the highest precision and accuracy is of a challenging task. This paper proposes ensemble learning using a soft voting approach to improve the accuracy of detection. First, we trained five individual classifiers, namely adaptive neuro-fuzzy inference system (ANFIS), random forest, multilayer perceptron (MLP), k-nearest neighbor (k-NN), and support vector machine (SVM) on the same urine dataset. We then created new ensemble learning using the soft voting approach by averaging the probability of individual ANFIS, random forest, MLP, k-NN, and SVM. Firefly algorithm for weight optimization is used to strengthen individual classifiers to form an ensemble and increase the prediction accuracy. Our proposed ensemble produces an accuracy value of 100% compared to the individual classifiers mentioned above.