A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS USING FEATURE SELECTION METHODS FOR MOVIE REVIEW ANALYSIS

2017 ◽  
Vol 11 (3) ◽  
pp. 1
Author(s):  
KAUR RAJWINDER ◽  
VERMA PRINCE ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Selection Methods ◽  
Review Analysis

Identification and analysis of the cleavage site in a signal peptide using SMOTE, dagging, and feature selection methods

2018 ◽  
Vol 14 (1) ◽  
pp. 64-73 ◽  
Author(s):  
ShaoPeng Wang ◽  
Deling Wang ◽  
JiaRui Li ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Signal Peptide ◽  
Cleavage Site ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cleavage Sites ◽  
Selection Methods

Several machine learning algorithms were adopted to investigate cleavage sites in a signal peptide. An optimal dagging based classifier was constructed and 870 important features were deemed to be important for this classifier.

scholarly journals Phishing Detection Based on Machine Learning and Feature Selection Methods

2019 ◽  
Vol 13 (12) ◽  
pp. 171 ◽  
Author(s):  
Mohammad Almseidin ◽  
AlMaha Abu Zuraiq ◽  
Mouhammd Al-kasassbeh ◽  
Nidal Alnidami
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Selection Methods ◽  
Random Forest Algorithm ◽  
Phishing Detection ◽  
Enormous Number

With increasing technology developments, the Internet has become everywhere and accessible by everyone. There are a considerable number of web-pages with different benefits. Despite this enormous number, not all of these sites are legitimate. There are so-called phishing sites that deceive users into serving their interests. This paper dealt with this problem using machine learning algorithms in addition to employing a novel dataset that related to phishing detection, which contains 5000 legitimate web-pages and 5000 phishing ones. In order to obtain the best results, various machine learning algorithms were tested. Then J48, Random forest, and Multilayer perceptron were chosen. Different feature selection tools were employed to the dataset in order to improve the efficiency of the models. The best result of the experiment achieved by utilizing 20 features out of 48 features and applying it to Random forest algorithm. The accuracy was 98.11%.

scholarly journals Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 216
Author(s):  
Mi Luo ◽  
Yifu Wang ◽  
Yunhong Xie ◽  
Lai Zhou ◽  
Jingjing Qiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forests ◽  
Aboveground Biomass ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Selection Methods ◽  
Estimation Models

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

Sign in / Sign up

Export Citation Format

Share Document