scholarly journals A Novel Robust Fuzzy Rough Set Model for Feature Selection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuwen Li ◽  
Shoushui Wei ◽  
Xing Liu ◽  
Zhimin Zhang
Keyword(s):  
Feature Selection ◽  
Data Processing ◽  
Rough Set ◽  
Selection Algorithm ◽  
Fuzzy Rough Set ◽  
Data Processing Method ◽  
Proposed Model ◽  
Sample Set ◽  
Representative Samples ◽  
Sample Pair

The existing fuzzy rough set (FRS) models all believe that the decision attribute divides the sample set into several “clear” decision classes, and this data processing method makes the model sensitive to noise information when conducting feature selection. To solve this problem, this paper proposes a robust fuzzy rough set model (RS-FRS) based on representative samples. Firstly, the fuzzy membership degree of the samples is defined to reflect its fuzziness and uncertainty, and RS-FRS model is constructed to reduce the influence of the noise samples. RS-FRS model does not need to set parameters for the model in advance and can effectively reduce the complexity of the model and human intervention. On this basis, the related properties of RS-FRS model are studied, and the sample pair selection algorithm (SPS) based on RS-FRS is used for feature selection. In this paper, RS-FRS is tested and analysed on the open 12 datasets. The experimental results show that RS-FRS model proposed can effectively select the most relevant features and has certain robustness to the noise information. The proposed model has a good applicability for data processing and can effectively improve the performance of feature selection.

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

Dynamic interaction feature selection based on fuzzy rough set

2021 ◽  
Author(s):  
Jihong Wan ◽  
Hongmei Chen ◽  
Tianrui Li ◽  
Xiaoling Yang ◽  
BinBin Sang
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Dynamic Interaction ◽  
Fuzzy Rough Set

A novel fuzzy rough set based long short-term memory integration model for energy consumption prediction of public buildings

2020 ◽  
pp. 1-15
Author(s):  
Hongchang Sun ◽  
Yadong wang ◽  
Lanqiang Niu ◽  
Fengyu Zhou ◽  
Heng Li
Keyword(s):  
Energy Consumption ◽  
Rough Set ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Short Term ◽  
Fuzzy Rough Set ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models.

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.

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