An improved tree model based on ensemble feature selection for classification

Abstract

Researchers train and build specific models to classify the presence and absence of a disease and the accuracy of such classification models is continuously improved. The process of building a model and training depends on the medical data utilized. Various machine learning techniques and tools are used to handle different data with respect to disease types and their clinical conditions. Classification is the most widely used technique to classify disease and the accuracy of the classifier largely depends on the attributes. The choice of the attribute largely affects the diagnosis and performance of the classifier. Due to growing large volumes of medical data across different clinical conditions, the need for choosing relevant attributes and features still lacks method to handle datasets that target specific diseases. This study uses an ensemble-based feature selection using random trees and wrapper method to improve the classification. The proposed ensemble learning classification method derives a subset using the wrapper method, bagging, and random trees. The proposed method removes the irrelevant features and selects the optimal features for classification through probability weighting criteria. The improved algorithm has the ability to distinguish the relevant features from irrelevant features and improve the classification performance. The proposed feature selection method is evaluated using SVM, RF, and NB evaluators and the performances are compared against the FSNBb, FSSVMb, GASVMb, GANBb, and GARFb methods. The proposed method achieves mean classification accuracy of 92% and outperforms the other ensemble methods.