Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems

Frontiers in Public Health

article

Sashikala Mishra¹  Kailash Shaw¹  Debahuti Mishra²  Shruti Patil³  Ketan Kotecha³  Satish Kumar³  Simi Bajaj⁴ 

[1] Symbiosis Institute of Technology, Symbiosis International University;Department of Computer Science and Engineering, Siksha O Anusandhan Deemed to be University;Symbiosis Centre for Applied Artificial Intelligence ,(SCAAI), Symbiosis Institute of Technology, Symbiosis International ,(Deemed University);School of Computer Data and Mathematical Sciences, University of Western Sydney

关键词: bit-fusion ensemble algorithm;    classifier fusion;    k-nearest neighbor;    Multi-Layer Perceptron;    Naïve Bayesian classifier;    support vector machine;   

DOI  :  10.3389/fpubh.2022.858282

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates.

【 授权许可】

CC BY