Kwara State University
Saidat Olaniran
Designation: Lecturer II
Department: Statistics and Mathematical Sciences
| My Publications | ||||||||
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| S/N | Title | Abstract | Authors | Volume Numbers | Publication Type | Publication Date | Link | |
| 1 | Empirical Bayesian Binary Classification Forests Using Bootstrap Prior. |
In this paper, we present a new method called Empirical Bayesian Random Forest (EBRF) for binary classification problem. The prior ingredient for the method was obtained using the bootstrap prior technique. EBRF addresses explicitly low accuracy problem in Random Forest (RF) classifier when the number of relevant input variables is relatively lower compared to the total number of input variables. The improvement was achieved by replacing the arbitrary subsample variable size with empirical Bayesian estimate. An illustration of the proposed and existing methods was performed using five high-dimensional microarray datasets that emanated from the colon, breast, lymphoma, and Central Nervous System (CNS) cancer tumors. Results from the data analysis revealed that EBRF provides reasonably higher accuracy, sensitivity, specificity, and Area Under Receiver Operating Characteristics Curve (AUC) than RF in most of the datasets used. | Olaniran, O. R., Abdullah, M. A. A., Pillay, K. G., & Olaniran, S. F. | 7 (4.30) 2018 170-175 | International (Scopus) | 2018-12-03 | http://dx.doi.org/10.14419/ijet.v7i4.30.22104 | |