Comparative Evaluation of Several Classification Algorithms on News Posts Using Reddit Social Network Dataset

2021 ◽  
pp. 1003-1026
Author(s):  
Ahmad Rawashdeh ◽  
Mohammad Rawashdeh ◽  
Omar Rawashdeh
Keyword(s):  
Social Network ◽  
Comparative Evaluation ◽  
Classification Algorithms

scholarly journals Mucinous Adenocarcinoma Involving the Ovary: Comparative Evaluation of the Classification Algorithms using Tumor Size and Laterality

2010 ◽  
Vol 25 (2) ◽  
pp. 220 ◽  
Author(s):  
Eun Sun Jung ◽  
Jeong Hoon Bae ◽  
Ahwon Lee ◽  
Yeong Jin Choi ◽  
Jong-Sup Park ◽  
...  
Keyword(s):  
Tumor Size ◽  
Mucinous Adenocarcinoma ◽  
Comparative Evaluation ◽  
Classification Algorithms

A Comparative Analysis of Classification Algorithms on Students’ Performance

2020 ◽  
Vol 8 (2) ◽  
pp. 20-34
Author(s):  
Nilar Aye
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Comparative Evaluation ◽  
Classification Algorithms ◽  
Large Dataset ◽  
Classification Technique ◽  
Education Data ◽  
Future Events ◽  
Student’S Performance ◽  
Events Data

Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool.  This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.

scholarly journals Identifying Bots in Social Networks Using the Example of LiveJournal

2020 ◽  
Vol 14 (2) ◽  
pp. 44-50
Author(s):  
A. A. Kochkarov ◽  
N. V. Kalashnikov ◽  
R. A. Kochkarov
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Comparative Analysis ◽  
Online Games ◽  
Classification Algorithms ◽  
Internet Users

Social networks have firmly entered the lives of billions of global Internet users worldwide. They communicate in social networks, play online games, make purchases, organise online events — exchange content from all walks of life [1, 2]. The most popular and well-known services in Russia are Vkontakte (vk.com), Youtube.com, Facebook.com, Odnoklassniki (Ok.ru), etc. The interfaces of such platforms allo — fake accounts. In this paper, we propose an approach to detect bots using the LiveJournal social network as an example. For this, we investigated the characteristics of the user’s egograph and performed a comparative analysis of the results of the classification algorithms.

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