Feature Selection and Random Forest Classification for Breast Cancer Disease

2021 ◽  
pp. 191-210
Author(s):  
Shubham Raj ◽  
Swati Singh ◽  
Avinash Kumar ◽  
Sobhangi Sarkar ◽  
Chittaranjan Pradhan
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Cancer Disease ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Loan Amount Prediction Using Machine Learning

2021 ◽  
pp. 212-215
Author(s):  
Bhagyashri Rajesh Jawale ◽  
Priyanka Anil Badgujar ◽  
Rita Dnyaneshwar Talele ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Research Work ◽  
Ensemble Methods ◽  
Classification Algorithm ◽  
Optimization Techniques ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Or Organization ◽  
Loan Amount

Loan amount prediction is helpful for banks or organization who want their work easier. All Banks give Loan to customer and customer first apply for loan after any bank or organization validate customer information. It must be providing some advantages for banks or company or any organization who wants to give loan. There are various methods to improve the accuracy classification algorithm. The accuracy of random forest classification algorithm can be improved using Ensemble methods. Optimization techniques and Feature selection methods available. In this research work novel hybrid feature selection algorithm using wrapper model and fisher introduced. The main objective of this paper is to prove that new hybrid model produces better accuracy than the traditional random forest algorithm.

scholarly journals Nonlinear Random Forest Classification, a Copula-Based Approach

2021 ◽  
Vol 11 (15) ◽  
pp. 7140
Author(s):  
Radko Mesiar ◽  
Ayyub Sheikhi
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Linear Function ◽  
Simulation Study ◽  
Random Forest Algorithm ◽  
Real Dataset ◽  
Random Forest Classification ◽  
Forest Classification

In this work, we use a copula-based approach to select the most important features for a random forest classification. Based on associated copulas between these features, we carry out this feature selection. We then embed the selected features to a random forest algorithm to classify a label-valued outcome. Our algorithm enables us to select the most relevant features when the features are not necessarily connected by a linear function; also, we can stop the classification when we reach the desired level of accuracy. We apply this method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset.

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