Reducing the Uncertainty of Agile Software Development Using a Random Forest Classification Algorithm

2021 ◽  
pp. 145-155
Author(s):  
Ewelina Wińska ◽  
Estera Kot ◽  
Włodzimierz Dąbrowski
Keyword(s):  
Random Forest ◽  
Software Development ◽  
Agile Software Development ◽  
Classification Algorithm ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Agile Software

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 Plant Identification Via Leaf Classification Using Color and Biometric Features

2021 ◽  
Vol 5 (2) ◽  
pp. 393-400
Author(s):  
Çiğdem TURHAL
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Recognition Rate ◽  
Classification Algorithm ◽  
Plant Identification ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Living Things ◽  
Short Period ◽  
Biometric Features

Plants that are of great importance for humans and other living things are an integral part of our ecosystem. In today's world, where many plant species are at risk of disappearance, the identification of plants helps to protect and survive all natural life. There are many studies presented in the literature for plant identification. The most popular of these identification methods is leaf based classification. The reason for choosing leaves in this classification is that they are easier to obtain than other biometric components such as flowers available for a short period of time. Various biometric properties of the leaf must be determined for leaf classifications. In traditionally it is time consuming and expensive to perform this process visually by experts. In this article, various leaf biometric features obtained by digital image processing methods are used as the feature extraction step for automatic leaf classification. As the classification algorithms, Naive Bayes, Linear Regression, Multilayer Perceptron, Decision Tree and Random Forest are used. According to the experimental results using the training set as the test set, 100% recognition rate is obtained for Random Forest classification algorithm and 96% recognition rate is obtained in 30-fold cross validation for Linear Regression classification algorithm.

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