scholarly journals Feature Selection of Grey Wolf Optimizer Based on Quantum Computing and Uncertain Symmetry Rough Set

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1470
Author(s):  
Guobao Zhao ◽  
Haiying Wang ◽  
Deli Jia ◽  
Quanbin Wang
Keyword(s):  
Feature Selection ◽  
Quantum Computing ◽  
Rough Set ◽  
Classification Accuracy ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Local Searches

Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to feature selection, and each of them owned the unique advantages of optimizing feature selection algorithm. Quantum computing had a good balance ability when exploring feature sets between global and local searches. Grey wolf optimizer could effectively explore all possible feature subsets, and uncertain symmetry rough set theory could accurately evaluate the correlation of potential feature subsets. QCGWORS intelligent algorithm could minimize the number of features while maximizing classification performance. In the experimental stage, k nearest neighbors (KNN) classifier and random forest (RF) classifier guided the machine learning process of the proposed algorithm, and 13 datasets were compared for testing experiments. Experimental results showed that compared with other feature selection methods, QCGWORS improved the classification accuracy on 12 datasets, among which the best accuracy was increased by 20.91%. In attribute reduction, each dataset had a benefit of the reduction effect of the minimum feature number.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

A Weighted Naive Bayes Algorithm Based on the Attribute Order Reduction

2013 ◽  
Vol 718-720 ◽  
pp. 2108-2112 ◽  
Author(s):  
Xi Zhou ◽  
Ke Luo
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Attribute Reduction ◽  
Order Reduction ◽  
Classification Performance ◽  
Classification Method ◽  
Bayes Classifier ◽  
Attribute Order ◽  
Bayes Algorithm

Naïve Bayes classifier was generally considered as a simple and efficient classification method. However, its classification performance was affected to some extent because of the assuming that the conditions properties were independent of each other. By analyzing the classification principle and improvement of Bayesian and the Attribute Reduction of Rough Set, this paper proposed a Naïve Bayes algorithm that the attribute order reduction and weighting were improved simultaneously. Experiment results demonstrated that the proposed method performed well in classification accuracy.

scholarly journals K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining

2019 ◽  
Vol 1 (2) ◽  
pp. 23-35
Author(s):  
Dwi Normawati ◽  
Dewi Pramudi Ismi
Keyword(s):  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Rough Set ◽  
Cross Validation ◽  
Rough Set Theory ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Rule Based ◽  
Fold Cross Validation

Coronary heart disease is a disease that often causes human death, occurs when there is atherosclerosis blocking blood flow to the heart muscle in the coronary arteries. The doctor's referral method for diagnosing coronary heart disease is coronary angiography, but it is invasive, high risk and expensive. The purpose of this study is to analyze the effect of implementing the k-Fold Cross Validation (CV) dataset on the rule-based feature selection to diagnose coronary heart disease, using the Cleveland heart disease dataset. The research conducted a feature selection using a medical expert-based (MFS) and computer-based method, namely the Variable Precision Rough Set (VPRS), which is the development of the Rough Set theory. Evaluation of classification performance using the k-Fold method of 10-Fold, 5-Fold and 3-Fold. The results of the study are the number of attributes of the feature selection results are different in each Fold, both for the VPRS and MFS methods, for accuracy values obtained from the average accuracy resulting from 10-Fold, 5-Fold and 3-Fold. The result was the highest accuracy value in the VPRS method 76.34% with k = 5, while the MTF accuracy was 71.281% with k = 3. So, the k-fold implementation for this case is less effective, because the division of data is still structured, according to the order of records that apply in each fold, while the amount of testing data is too small and too structured. This affects the results of the accuracy because the testing rules are not thoroughly represented

BINARY PSO AND ROUGH SET THEORY FOR FEATURE SELECTION: A MULTI-OBJECTIVE FILTER BASED APPROACH

2014 ◽  
Vol 13 (02) ◽  
pp. 1450009 ◽  
Author(s):  
BING XUE ◽  
LIAM CERVANTE ◽  
LIN SHANG ◽  
WILL N. BROWNE ◽  
MENGJIE ZHANG
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Binary Particle Swarm Optimization ◽  
Traditional Methods ◽  
K Nearest Neighbors ◽  
Multi Objective ◽  
Single Objective

Feature selection is a multi-objective problem, where the two main objectives are to maximize the classification accuracy and minimize the number of features. However, most of the existing algorithms belong to single objective, wrapper approaches. In this work, we investigate the use of binary particle swarm optimization (BPSO) and probabilistic rough set (PRS) for multi-objective feature selection. We use PRS to propose a new measure for the number of features based on which a new filter based single objective algorithm (PSOPRSE) is developed. Then a new filter-based multi-objective algorithm (MORSE) is proposed, which aims to maximize a measure for the classification performance and minimize the new measure for the number of features. MORSE is examined and compared with PSOPRSE, two existing PSO-based single objective algorithms, two traditional methods, and the only existing BPSO and PRS-based multi-objective algorithm (MORSN). Experiments have been conducted on six commonly used discrete datasets with a relative small number of features and six continuous datasets with a large number of features. The classification performance of the selected feature subsets are evaluated by three classification algorithms (decision trees, Naïve Bayes, and k-nearest neighbors). The results show that the proposed algorithms can automatically select a smaller number of features and achieve similar or better classification performance than using all features. PSOPRSE achieves better performance than the other two PSO-based single objective algorithms and the two traditional methods. MORSN and MORSE outperform all these five single objective algorithms in terms of both the classification performance and the number of features. MORSE achieves better classification performance than MORSN. These filter algorithms are general to the three different classification algorithms.

scholarly journals An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 155 ◽  
Author(s):  
Lin Sun ◽  
Xiaoyu Zhang ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Data Sets ◽  
Complex Data ◽  
Decision Systems ◽  
Neighborhood Rough Sets

Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.

scholarly journals A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hua Li ◽  
Deyu Li ◽  
Yanhui Zhai ◽  
Suge Wang ◽  
Jing Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Mathematical Tool ◽  
Knowledge Reduction ◽  
Variable Precision ◽  
Multilabel Learning ◽  
Decision Tables

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated withδ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.

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.

Social network analysis of law information privacy protection of cybersecurity based on rough set theory

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Li ◽  
Xuhua Hu
Keyword(s):  
Feature Selection ◽  
Social Network ◽  
Set Theory ◽  
Rough Set ◽  
Privacy Protection ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Practical Significance ◽  
Network Environment ◽  
Content Type

Purpose The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance. Design/methodology/approach In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed. Findings The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment. Research limitations/implications In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification. Practical implications In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining. Originality/value In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

scholarly journals Research on Feature Selection/Attribute Reduction Method Based on Rough Set Theory

2019 ◽  
Vol 154 ◽  
pp. 194-198
Author(s):  
Shi Qiang Wang ◽  
Cai Yun Gao ◽  
Chang Luo ◽  
Gui Mei Zheng ◽  
Yan Nian Zhou
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction
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