A*-Reduct: A Heuristic Rough Set Based Feature Selection Algorithm and Its Application to Text Summarization

2020 ◽  
pp. 239-245
Author(s):  
Nidhika Yadav ◽  
Niladri Chatterjee
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Text Summarization ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

An exact feature selection algorithm based on rough set theory

Complexity ◽  
2014 ◽  
Vol 20 (5) ◽  
pp. 50-62 ◽  
Author(s):  
Mohammad Taghi Rezvan ◽  
Ali Zeinal Hamadani ◽  
Seyed Reza Hejazi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

Rough Set-hypergraph-based Feature Selection Approach for Intrusion Detection Systems

2016 ◽  
Vol 66 (6) ◽  
pp. 612 ◽  
Author(s):  
M.R. Gauthama Raman ◽  
K. Kannan ◽  
S.K. Pal ◽  
V. S. Shankar Sriram
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Rough Set ◽  
Network Architecture ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Detection Systems ◽  
Helly Property

Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasizes the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. In this paper, a novel rough set κ-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier’s performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.

Fast feature selection algorithm for neighborhood rough set model based on Bucket and Trie structures

2019 ◽  
Vol 5 (3) ◽  
pp. 329-347 ◽  
Author(s):  
Rachid Benouini ◽  
Imad Batioua ◽  
Soufiane Ezghari ◽  
Khalid Zenkouar ◽  
Azeddine Zahi
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Model Based ◽  
Neighborhood Rough Set

Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs

2019 ◽  
Vol 497 ◽  
pp. 77-90 ◽  
Author(s):  
K Selvakumar ◽  
Marimuthu Karuppiah ◽  
L SaiRamesh ◽  
SK Hafizul Islam ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Rough Set ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Fuzzy Rough Set

scholarly journals Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 704
Author(s):  
Jiucheng Xu ◽  
Kanglin Qu ◽  
Meng Yuan ◽  
Jie Yang
Keyword(s):  
Information Theory ◽  
Feature Selection ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classification Performance ◽  
Joint Entropy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Selection Algorithms ◽  
Theory View

Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.

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