Covering Arrays to Support the Process of Feature Selection in the Random Forest Classifier

2019 ◽  
pp. 64-76
Author(s):  
Sebastián Vivas ◽  
Carlos Cobos ◽  
Martha Mendoza
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Covering Arrays

Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

2017 ◽  
Vol 60 ◽  
pp. 42-49 ◽  
Author(s):  
Desbordes Paul ◽  
Ruan Su ◽  
Modzelewski Romain ◽  
Vauclin Sébastien ◽  
Vera Pierre ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Random Forest ◽  
Oesophageal Cancer ◽  
Outcome Prediction ◽  
Random Forest Classifier ◽  
Selection For

scholarly journals A Comparative Study using Feature Selection to Predict the Behaviour of Bank Customers

2020 ◽  
Vol 184 ◽  
pp. 01011
Author(s):  
Sreethi Musunuru ◽  
Mahaalakshmi Mukkamala ◽  
Latha Kunaparaju ◽  
N V Ganapathi Raju
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Customer Behavior ◽  
Machine Learning Algorithms ◽  
The Status ◽  
Personal Level ◽  
Near Future ◽  
Structure Communication

Though banks hold an abundance of data on their customers in general, it is not unusual for them to track the actions of the creditors regularly to improve the services they offer to them and understand why a lot of them choose to exit and shift to other banks. Analyzing customer behavior can be highly beneficial to the banks as they can reach out to their customers on a personal level and develop a business model that will improve the pricing structure, communication, advertising, and benefits for their customers and themselves. Features like the amount a customer credits every month, his salary per annum, the gender of the customer, etc. are used to classify them using machine learning algorithms like K Neighbors Classifier and Random Forest Classifier. On classifying the customers, banks can get an idea of who will be continuing with them and who will be leaving them in the near future. Our study determines to remove the features that are independent but are not influential to determine the status of the customers in the future without the loss of accuracy and to improve the model to see if this will also increase the accuracy of the results.

scholarly journals Feature Selection using Random Forest Classifier for Predicting Prostate Cancer

2019 ◽  
Vol 546 ◽  
pp. 052031 ◽  
Author(s):  
Mia Huljanah ◽  
Zuherman Rustam ◽  
Suarsih Utama ◽  
Titin Siswantining
Keyword(s):  
Prostate Cancer ◽  
Feature Selection ◽  
Random Forest ◽  
Random Forest Classifier
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