Multiple aggregation model using Ensembles Classifier for feature selection: A Homogeneous Approach

A device's substantial success is highly dependent on its users and their interaction with it. The characteristics of the user's database and a thorough understanding of their expectations are critical for developing a device that works. This entails developing a tool that utilizes a homogeneous ensemble feature selector to achieve the same or improved performance on datasets with fewer features. This feature is in support of improving the device performance. In this article, we used the Ensemble classification model for feature selection in the context of machine learning. One of the ML supervised classification models, bagging and boosting, is utilized to work on bootstrap training set samples. This generates a device and runs the user's dataset through feature selectors to get feature rankings. These ranks are then combined using various rank aggregation algorithms to determine the most important attributes in the dataset. This device implements a device that runs the user's dataset through feature selectors and returns feature ranks. These ranks are then merged with the help of various rank aggregation techniques to discover the most important qualities in the supplied dataset.