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.

scholarly journals Content analytics based on random forest classification technique: An empirical evaluation using online news dataset

2021 ◽  
Vol 8 (2) ◽  
pp. 77-84
Author(s):  
Nohuddin et al. ◽  
Keyword(s):  
Random Forest ◽  
Empirical Evaluation ◽  
Online News ◽  
Document Management ◽  
Random Forest Algorithm ◽  
Random Forest Classification ◽  
Classification Technique ◽  
Forest Classification ◽  
Management Procedures ◽  
Text Content

In this paper, a study is established for exploiting a document classification technique for categorizing a set of random online documents. The technique is aimed to assign one or more classes or categories to a document, making it easier to manage and sort. This paper describes an experiment on the proposed method for classifying documents effectively using the decision tree technique. The proposed research framework is a Document Analysis based on the Random Forest Algorithm (DARFA). The proposed framework consists of 5 components, which are (i) Document dataset, (ii) Data Preprocessing, (iii) Document Term Matrix, (iv) Random Forest classification, and (v) Visualization. The proposed classification method can analyze the content of document datasets and classifies documents according to the text content. The proposed framework use algorithms that include TF-IDF and Random Forest algorithm. The outcome of this study benefits as an enhancement to document management procedures like managing documents in daily business operations, consolidating inventory systems, organizing files in databases, and categorizing document folders.

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.

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 Research and Implementation on Power Analysis Attacks for Unbalanced Data

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaoyi Duan ◽  
Dong Chen ◽  
Xiaohong Fan ◽  
Xiuying Li ◽  
Ding Ding ◽  
...  
Keyword(s):  
Random Forest ◽  
Classification Accuracy ◽  
Power Analysis ◽  
Prediction Accuracy ◽  
Random Forest Algorithm ◽  
Training Set ◽  
Accuracy Rate ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Weight Model

In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories has increased from 0% to 100%.

scholarly journals Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas

2019 ◽  
Author(s):  
Natalie Weed ◽  
Trygve Bakken ◽  
Nile Graddis ◽  
Nathan Gouwens ◽  
Daniel Millman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Random Forest ◽  
Genetic Markers ◽  
Mouse Brain ◽  
Brain Atlas ◽  
Random Forest Algorithm ◽  
Random Forest Classification ◽  
Cortical Areas ◽  
Forest Classification ◽  
Allen Mouse Brain Atlas

AbstractThe mammalian neocortex is subdivided into a series of ‘cortical areas’ that are functionally and anatomically distinct, and are often distinguished in brain sections using histochemical stains and other markers of protein expression. We searched the Allen Mouse Brain Atlas, a database of gene expression, for novel markers of cortical areas. We employed a random forest algorithm to screen for genes that change expression at area borders. We found novel genetic markers for 19 of 39 areas and provide code that quickly and efficiently searches the Allen Mouse Brain Atlas.

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