A Comparative Study on Feature Selection Techniques for Multi-cluster Text Data

2018 ◽  
pp. 203-215
Author(s):  
Ananya Gupta ◽  
Shahin Ara Begum
Keyword(s):  
Feature Selection ◽  
Comparative Study ◽  
Text Data ◽  
Feature Selection Techniques

A Comparative Study on the Feature Selection Techniques for Intrusion Detection System

2019 ◽  
Vol 8 (1) ◽  
pp. 42-47
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Support Vector ◽  
Network Intrusion ◽  
Chi Square Analysis ◽  
Feature Selection Techniques

The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today world situations. This article describes the comparative study on the Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square analysis feature selection techniques with different Classification methods like Artificial Neural Network, Naïve Bayes and Support Vector Machine. In this article, different performance metrics has utilized to choose the appropriate Feature Selection method for better data classification in IDS.

A Brief Study of Approaches to Text Feature Selection

2018 ◽  
pp. 216-243
Author(s):  
Ravindra Babu Tallamaraju ◽  
Manas Kirti
Keyword(s):  
Feature Selection ◽  
Comparative Study ◽  
Social Networking ◽  
Text Data ◽  
Social Networking Services ◽  
Storage Devices ◽  
Text Feature ◽  
Wide Range ◽  
Efficiency And Effectiveness ◽  
Language Text

With reducing cost of storage devices, increasing amounts of data is being stored and processed for extracting intelligence. Classification and clustering have been two major approaches in generating data abstraction. Over the last few years, text data is dominating the types of data shared and stored. Some of the sources of such datasets are mobile data, e-commerce, and wide-range of continuously expanding social-networking services. Within each of these sources, the nature of data differs drastically from formal language text to Twitter or SMS slangs thereby leading to the need for different ways of processing the data for making meaningful summarization. Such summaries could effectively be used for business advantage. Processing of such data requires identifying appropriate set of features both for efficiency and effectiveness. In the current Chapter, we propose to discuss approaches to text feature selection and make a comparative study.

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