Performance enrichment through parameter tuning of random forest classification for imbalanced data applications

One of the foremost application domains in today's real-life scenario is unequal data distribution within datasets, related classifiers, and its Performance Enrichment Techniques (PET). Random Forest Classification (RFC) is one of the most efficient techniques that can function speedily over binary or multiclass imbalanced characteristics datasets. With its built-in ensemble capacity, building a generalized model on any Binary Imbalanced Datasets (BID) and Multiclass Imbalanced Datasets (MID) gets much easier. Related work carried out here implies that the attention of researchers is inclined towards ID applications and related machine learning techniques. RFC gives improvised performance due to Ensemble Approach (EA). EA generates several classifiers and segregates the results as PET. The performance of the single classifier is lower than the performance of the set of multiple classifiers. The associated study in this research paper provides insight into three PET, which are the count of folds in cross validation, variable count of trees and the feature selection method through Regression Analysis (RA) and it's tuning with RFC Classifier. Time and Cost required for classification is saved with the help of RA. Extensive experimental analysis carried out with a predictive modeler Salford System with proposed PET and tested on 32 benchmark datasets, which justifies the enhanced performance of RFC.Copyright (c) 2022 Elsevier Ltd. All rights reserved.Selection and peer-review under responsibility of the scientific committee of the First International Conference on Design and Materials (ICDM)-2021