Publication Type : Journal Article
Publisher : Journal of Chemical and Pharmaceutical Sciences
Source : Journal of Chemical and Pharmaceutical Sciences, Issue 4, p.14-17 (2016)
Url : https://www.jchps.com/specialissues/2016%20Special%20Issue%204/0820916.pdf
Keywords : Accuracy measures., Chemical compounds, classification, DRUGS, Machine learning, Medicinal plants, Rotation Forest
Campus : Coimbatore, Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science, Mathematics
Year : 2016
Abstract : Drug Discovery from medicinal plants is an important area in current research and has been providing important source of new drug leads. Plant extracts are proved as main source for many drugs. A major part of traditional therapy uses plant extracts or the associated active principles. Many of the traditional medicines are made as a result of applying some small synthetic modifications of naturally obtained substances. But most of the modern medicines are using synthetic substances instead of natural substances obtained from medicinal plants. Few machinelearning predictive algorithms are applied to classify the compounds to the defined classes and the accuracies of different classification algorithms are analyzed. The present study shows the significance of Rotation Forest ensemble algorithms in the classification of medicinal plant compounds. The other algorithms analyzed are Decision Tree, Random Forest and Naive Bayes. The Random Forest tree based ensemble outperformed the other algorithms in this study.
Cite this Research Publication : Ani R. and Dr. Deepa Gopakumar O. S., “Rotation Forest Ensemble Algorithm for the Classification of Phytochemicals from the Medicinal Plants”, Journal of Chemical and Pharmaceutical Sciences, no. 4, pp. 14-17, 2016.