scholarly journals Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

2019 ◽  
Vol 12 (1) ◽  
pp. 30-39
Author(s):  
Kongara Deepika ◽  
◽  
Nallamothu Sathyanarayana ◽  
Keyword(s):  
Feature Selection ◽  
Academic Performance ◽  
Random Forest ◽  
Performance Prediction ◽  
Student Academic Performance ◽  
Selection For

scholarly journals FEATURE SELECTION FOR AUTOMATIC DETECTION OF TEXT GENRE

2021 ◽  
pp. 84-90
Author(s):  
S. Sh. Shanshar ◽  
I. M. Ualiyeva
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Information Society ◽  
Automatic Detection ◽  
Practical Method ◽  
Automatic Recognition ◽  
Logistics Regression ◽  
Text Genre ◽  
Selection For

This article discusses the algorithms that can be used in the study and analysis of symbols to determine the genre of texts. There are differences in defining the genre of texts. Algorithm is also defined by describing the text, removing unnecessary characters, leaving only the text, and comparing it with the database. The article describes a practical method of automatic recognition of the text genre based on all parameters. Comparing the logistics regression, solution tree, random forest, MLPClassifier, AdaBoostClassifier, svm, GaussianNB algorithms, the choice of the most important parameters for the texts was considered. Defining the genre of texts is now relevant in all areas of the information society.

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