Descriptive analysis on bagging based hybrid ensemble classification technique

Abstract

Machine learning involves data mining, which solves many problems in data science. An application in machine learning predicts the outcome based on available data. There are many predictive strategies available. The most important method is dividing the most powerful predictions. Some of them predict the results satisfactorily and some are accurately measured. This investigation has carried out a process called the bagging based hybrid ensemble process, which collects the accuracy of weak algorithms by combining multiple separators for development. This study helps us to see the integration process that improves the accuracy of predictor birth. This is not only the study of weak separation algorithms, but also the use of algorithms using medical data, which predicts prematurely. This study proves to be an effective method of bagging based classification to improve the prediction accuracy of 97.4%.