scholarly journals Multi-label feature selection based on fuzzy neighborhood rough sets

2022 ◽  
Author(s):  
Jiucheng Xu ◽  
Kaili Shen ◽  
Lin Sun
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Feature Selection Method ◽  
Conditional Entropy ◽  
Mixed Data ◽  
Multiple Perspectives ◽  
Approximation Accuracy ◽  
Upper Approximation ◽  
Decision Systems ◽  
Neighborhood Rough Set

AbstractMulti-label feature selection, a crucial preprocessing step for multi-label classification, has been widely applied to data mining, artificial intelligence and other fields. However, most of the existing multi-label feature selection methods for dealing with mixed data have the following problems: (1) These methods rarely consider the importance of features from multiple perspectives, which analyzes features not comprehensive enough. (2) These methods select feature subsets according to the positive region, while ignoring the uncertainty implied by the upper approximation. To address these problems, a multi-label feature selection method based on fuzzy neighborhood rough set is developed in this article. First, the fuzzy neighborhood approximation accuracy and fuzzy decision are defined in the fuzzy neighborhood rough set model, and a new multi-label fuzzy neighborhood conditional entropy is designed. Second, a mixed measure is proposed by combining the fuzzy neighborhood conditional entropy from information view with the approximate accuracy of fuzzy neighborhood from algebra view, to evaluate the importance of features from different views. Finally, a forward multi-label feature selection algorithm is proposed for removing redundant features and decrease the complexity of multi-label classification. The experimental results illustrate the validity and stability of the proposed algorithm in multi-label fuzzy neighborhood decision systems, when compared with related methods on ten multi-label datasets.

A novel hybrid feature selection method considering feature interaction in neighborhood rough set

2021 ◽  
pp. 107167
Author(s):  
Jihong Wan ◽  
Hongmei Chen ◽  
Zhong Yuan ◽  
Tianrui Li ◽  
Xiaoling Yang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Interaction ◽  
Neighborhood Rough Set

Fuzzy integrated rough set theory situation feature extraction of network security

2021 ◽  
Vol 40 (4) ◽  
pp. 8439-8450
Author(s):  
Dongmei Zhao ◽  
Huiqian Song ◽  
Hong Li
Keyword(s):  
Network Security ◽  
Parallel Algorithm ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Conditional Entropy ◽  
Recall Rate ◽  
Mixed Data ◽  
Rank Matrix ◽  
Neighborhood Rough Set

The element extraction from network security condition is the foundation security awareness. Its excellence directly disturbsentire security system performance. In this paper we introduce fuzzy logic based rough set theory for extracting security conditional factors. The traditional extraction method of network security situation elements relies on a lot of prior knowledge. With the purpose of solving this issue, in this paper we proposed fuzzy rough set theory based featurerank matrix of neighborhood rough set. Additionally, we propose reduction based parallel algorithm that uses the concept of conditional entropy in order to constructs the feature rank matrix as well as, constructs the core attribute by using reduction rules, takes the threshold of standard deviation as the threshold, and redefines the multi threshold neighborhood of mixed data. The attack type recognition training is carried out on lib SVM, filtered classifier, j48 and random tree classifiers respectively. The results demonstrate that the proposed reduction based parallel algorithm can increase the accuracy of classification, shorten the modeling time, and show increased recall rate and decreased false alarm rate.

scholarly journals Feature selection using self-information and entropy-based uncertainty measure for fuzzy neighborhood rough set

2021 ◽  
Author(s):  
Jiucheng Xu ◽  
Meng Yuan ◽  
Yuanyuan Ma
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Performance ◽  
High Dimensional ◽  
Joint Entropy ◽  
Uncertainty Measure ◽  
Entropy Model ◽  
Upper Approximation ◽  
Information Measures ◽  
Neighborhood Rough Set

AbstractFeature selection based on the fuzzy neighborhood rough set model (FNRS) is highly popular in data mining. However, the dependent function of FNRS only considers the information present in the lower approximation of the decision while ignoring the information present in the upper approximation of the decision. This construction method may lead to the loss of some information. To solve this problem, this paper proposes a fuzzy neighborhood joint entropy model based on fuzzy neighborhood self-information measure (FNSIJE) and applies it to feature selection. First, to construct four uncertain fuzzy neighborhood self-information measures of decision variables, the concept of self-information is introduced into the upper and lower approximations of FNRS from the algebra view. The relationships between these measures and their properties are discussed in detail. It is found that the fourth measure, named tolerance fuzzy neighborhood self-information, has better classification performance. Second, an uncertainty measure based on the fuzzy neighborhood joint entropy has been proposed from the information view. Inspired by both algebra and information views, the FNSIJE is proposed. Third, the K–S test is used to delete features with weak distinguishing performance, which reduces the dimensionality of high-dimensional gene datasets, thereby reducing the complexity of high-dimensional gene datasets, and then, a forward feature selection algorithm is provided. Experimental results show that compared with related methods, the presented model can select less important features and have a higher classification accuracy.

scholarly journals An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Zhang ◽  
Guang Lu ◽  
Jiaquan Li ◽  
Chuanwen Li
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Small Sample Size ◽  
High Dimensional Data ◽  
Classification Performance ◽  
Small Sample ◽  
Ensemble Classification ◽  
High Dimensional ◽  
Sample Classification ◽  
Neighborhood Rough Set

Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

Rough set-based feature selection method

2005 ◽  
Vol 15 (3) ◽  
pp. 280-284 ◽  
Author(s):  
Zhan Yanmei ◽  
Zeng Xiangyang ◽  
Sun Jincai
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Feature Selection Method ◽  
Selection Method

Intuitionistic Fuzzy Neighborhood Rough Set Model for Feature Selection

2018 ◽  
Vol 7 (2) ◽  
pp. 75-84 ◽  
Author(s):  
Shivam Shreevastava ◽  
Anoop Kumar Tiwari ◽  
Tanmoy Som
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Continuous Data ◽  
Feature Subset ◽  
Data Set ◽  
Intuitionistic Fuzzy ◽  
Neighborhood Models ◽  
Neighborhood Rough Set

Feature selection is one of the widely used pre-processing techniques to deal with large data sets. In this context, rough set theory has been successfully implemented for feature selection of discrete data set but in case of continuous data set it requires discretization, which may cause information loss. Fuzzy rough set theory approaches have also been used successfully to resolve this issue as it can handle continuous data directly. Moreover, almost all feature selection techniques are used to handle homogeneous data set. In this article, the center of attraction is on heterogeneous feature subset reduction. A novel intuitionistic fuzzy neighborhood models have been proposed by combining intuitionistic fuzzy sets and neighborhood rough set models by taking an appropriate pair of lower and upper approximations and generalize it for feature selection, supported with theory and its validation. An appropriate algorithm along with application to a data set has been added.

scholarly journals Rough set and teaching learning based optimization technique for optimal features selection

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Suresh Satapathy ◽  
Anima Naik ◽  
K. Parvathi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimization Technique ◽  
Feature Selection Method ◽  
Optimization Techniques ◽  
Features Selection ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

AbstractRough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

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