Empirical Bayesian Binary Classification Forests Using Bootstrap Prior

Abstract

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 colon, breast, lymphoma and Central Nervous System (CNS) cancer tumours. 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.

Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

“…They suggested objective prior through Bayes factor as an alternative approach to handle one-way ANOVA model. Objective Bayes (let the data speak for themselves) are no way better than the classical approach as its often used when subjective priors are difficult to compute or elicit (Yahya, Olaniran, and Ige 2014;Olaniran, Olaniran, Yahya, Banjoko, Garba, Amusa, and Gatta 2016;Olaniran and Yahya 2017;Olaniran, Abdullah, Pillay, and Olaniran 2018). Thus, in this paper, we developed a Bayesian one-way ANOVA test function using bootstrap prior Olaniran and Yahya (2017) for variable selection in multiclass classification problem.…”

A Novel Variable Selection Procedure for Binary Logistic Regression Using Akaike Information Criteria Testing:An Example in Breast Cancer Prediction: Methodological Study (Validity Study)

“…The goal of logistic regression is to find the best-fitting model that can predict the probability of the binary outcome based on the values of the independent variables. [10][11][12][13][14][15] The logistic regression model is formulated using a logistic function, which is a type of sigmoid function that maps any real-valued input to the range [0, 1]. The logistic function is defined as: (1) where also denoted by is the probability of the dependent variable (breast cancer outcomes) being equal to 1 given the values of the independent variables and their associated coefficient , and is the exponential function.…”