scholarly journals Generating Term Weighting Schemes Through Genetic Programming

2019 ◽  
pp. 92-103
Author(s):  
Ahmad Mazyad ◽  
Fabien Teytaud ◽  
Cyril Fonlupt
Keyword(s):  
Genetic Programming ◽  
Term Weighting ◽  
Weighting Schemes

scholarly journals Improving Term Weighting Schemes for Short Text Classification in Vector Space Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166578-166592
Author(s):  
Surender Singh Samant ◽  
N. L. Bhanu Murthy ◽  
Aruna Malapati
Keyword(s):  
Vector Space ◽  
Text Classification ◽  
Vector Space Model ◽  
Term Weighting ◽  
Weighting Schemes ◽  
Short Text ◽  
Space Model

scholarly journals Classification of Online Grooming on Chat Logs Using Two Term Weighting Schemes

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Nur Rafeeqkha Sulaiman ◽  
Maheyzah Md. Siraj
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Support Vector ◽  
Term Weighting ◽  
Human Errors ◽  
Weighting Schemes ◽  
Intelligence Gathering ◽  
Internet Users ◽  
Document Frequency

Due to the growth of Internet, it has not only become the medium for getting information, it has also become a platform for communicating. Social Network Service (SNS) is one of the main platform where Internet users can communicate by distributing, sharing of information and knowledge. Chatting has become a popular communication medium for Internet users whereby users can communicate directly and privately with each other. However, due to the privacy of chat rooms or chatting mediums, the content of chat logs is not monitored and not filtered. Thus, easing cyber predators preying on their preys. Cyber groomers are one of cyber predators who prey on children or minors to satisfy their sexual desire. Workforce expertise that involve in intelligence gathering always deals with difficulty as the complexity of crime increases, human errors and time constraints. Hence, it is difficult to prevent undesired content, such as grooming conversation, in chat logs. An investigation on two term weighting schemes on two datasets are used to improve the content-based classification techniques. This study aims to improve the content-based classification accuracy on chat logs by comparing two term weighting schemes in classifying grooming contents. Two term weighting schemes namely Term Frequency – Inverse Document Frequency – Inverse Class Space Density Frequency (TF.IDF.ICSdF) and Fuzzy Rough Feature Selection (FRFS) are used as feature selection process in filtering chat logs. The performance of these techniques were examined via datasets, and the accuracy of their result was measured by Support Vector Machine (SVM). TF.IDF.ICSdF and FRFS are judged based on accuracy, precision, recall and F score measurement.

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