Rough Set Attribute Reduction Algorithm Research Articles

Attribute Reduction of Neighborhood Rough Set Based on Discernment

For neighborhood rough set attribute reduction algorithms based on dependency degree, a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed. The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes, allowing for a more accurate measurement of the importance degrees of attributes. Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.

Research on prediction model of converter temperature and carbon content based on spectral feature extraction

The flame of converter mouth can well reflect the change of temperature and composition of molten steel in the furnace. The flame characteristics of converter mouth collected by device can well predict the smelting process of converter. Based on the flame spectrum data set of converter mouth, this paper uses the BEADS algorithm and rough set attribute reduction algorithm optimized by genetic algorithm to extract the features of 2048-dimensional wavelength data. Through the model, eight indexes that contribute greatly to temperature and carbon content are selected, which are f-507, f-520, f-839, f-1073, f-1371, f-1528, f-1727 and f-1826. The MIC coefficients of the eight indicators with temperature and carbon content are calculated, and the MIC coefficients of the variables is small, and the selected indicators are representative. There was a significant correlation between temperature and C content. In BP neural network of temperature prediction model, it is found that the prediction accuracy of the training set is 0.99, the prediction accuracy of the test set is 0.99, the prediction accuracy of the verification set is 0.99, and the prediction accuracy of the whole set is 0.99. Through statistics, it is found that the hit rate of the temperature model in the range of ± 5 K is 88.7%, and the hit rate in the range of ± 10 K is 98.4%. and the RMSE parameter analysis shows that the average prediction error is 3.85 K. In BP neural network of carbon content prediction model, it is found that the prediction accuracy of the training set is 0.99, the prediction accuracy of the test set is 0.99, the prediction accuracy of the verification set is 0.98, and the prediction accuracy of the whole set is 0.99. Through statistics, it is found that the hit rate of the carbon contents model in the range of ± 0.05% is 94.0%, and the hit rate in the range of ± 0.10% is 98.3%, and the RMSE parameter analysis shows that the average prediction error is 0.021%. Finally, the universality of the model is verified by MIV algorithm.

A novel variable precision rough set attribute reduction algorithm based on local attribute significance

In attribute reduction, the method of partitioning information granules is crucial; however, the existing attribute reduction algorithms have limitations in this respect. To solve this problem, this study proposes the concept of an (α,β)-indiscernibility relation. This is a fresh partition approach for information granules, tolerating a certain degree of error information. On the basis of this concept, we propose a novel (α,β)-rough set model. Then we use this model to design a novel attribute reduction algorithm based on local attribute significance. To maintain the objectivity of this research and ensure fairness of the experiments, we present an integrated classifier to achieve the greatest classification accuracy. Ultimately, the experimental results verify that the fault-tolerance ability of information granules is increased. Moreover, the novel attribute reduction algorithm deletes redundant attributes and noise attributes to increase classification accuracy. In the later part of the experiment, different proportions of random Gaussian noise are added to each data set. The experimental results demonstrate the robustness and superiority of the new algorithm.

Attribute reduction algorithm of neighborhood rough set based on supervised granulation and its application

In neighborhood rough set theory, attribute reduction based on measure of information has important application significance. The influence of different decision classes was not considered for calculation of traditional conditional neighborhood entropy, and the improvement of algorithm based on conditional neighborhood entropy mainly includes of introducing multi granularity and different levels, while the mutual influence between samples with different labels is less considered. To solve this problem, this paper uses the supervised strategy to improve the conditional neighborhood entropy of three-layer granulation. By using two different neighborhood radii to adjust the mutual influence degree of different label samples, and by considering the mutual influence between conditional attributes through the feature complementary relationship, a neighborhood rough set attribute reduction algorithm based on supervised granulation is proposed. Experiment results on UCI data sets show that the proposed algorithm is superior to the traditional conditional neighborhood entropy algorithm in both aspects of reduction rate and reduction accuracy. Finally, the proposed algorithm is applied to the evaluation of fatigue life influencing factors of titanium alloy welded joints. The results of coupling relationship analysis show that the effect of joint type should be most seriously considered in the calculation of stress concentration factor. The results of influencing factors analysis show that the stress range has the highest weight among all the fatigue life influencing factors of titanium alloy welded joint.

Multi‐granularity decision rough set attribute reduction algorithm under quantum particle swarm optimization

The existing attribute reductions are carried out using equivalence relations under a complete information system, and there is less research on attribute reductions of incomplete information systems with new theoretical models such as multi-granularity decision rough sets. To address the above shortcomings, this paper first makes up a pessimistic-optimistic multi-granularity decision rough set model based on tolerance relations in incomplete information systems. The concepts of attribute importance and approximate distribution quality are introduced into the model to form an attribute reduction algorithm under incomplete information systems. Secondly, due to the NP-hard problem of attribute reduction, in order to further ensure the accuracy of the reduction result, this paper proposes a pessimistic-optimistic multi-granularity reduction algorithm under quantum particle swarm optimization. Experimental results on multiple-attribute data proved that the algorithm proposed in this paper can effectively attribute reduction in the decision table with missing data. At the same time, the algorithm of this paper has the role of iterative optimization search, ensuring the accuracy of the reduction results and increasing the applicability of multi-granularity decision rough sets.

Attribute Reduction Algorithm Based on Discrete Particle Swarm Optimization and Variable Precision Rough Set

Attribute reduction is proved to be a non-deterministic polynomial problem (NP). Minimal attribute reduction is the main research content in rough set theory. We find that in the traditional rough set attribute reduction algorithm, the rough set attribute reduction based on discrete particle swarm optimization (DPSO) performs well. However, the fitness function of this method has some limitations. When the minimum attribute reduction result is the conditional attribute set itself, the correct result cannot be obtained. For enhancing the accuracy and efficiency of attribute reduction, we propose an attribute reduction algorithm based on DPSO and variable precision rough set (VPRS). The proposed algorithm uses VPRS to process data more accurately. The new fitness function is constructed, and the attribute dependence is used as the function judgment basis. It can be adjusted automatically as the discrete binary particle swarm evolves, ensuring the convergence speed and evolution direction. Experimental results show that compared with traditional method, the proposed algorithm has stronger effectiveness and higher application value.

An Optimization of Master S-N Curve Fitting Method Based on Improved Neighborhood Rough Set

In this paper, a novel master S-N curve fitting method based on improved neighborhood rough set is proposed. The neighborhood relationship is used to granulate the fatigue information system of aluminum alloy welded joints, and the neighborhood rough set model is constructed from the information perspective. Based on conditional entropy, a neighborhood rough set attribute reduction algorithm (NRSBCE) is proposed. By means of this algorithm, comprehensive mathematical analysis model for fatigue life influencing factors is established and optimization of the S-N curve fitting method is introduced. In this work, the fatigue decision system of the aluminum welded joints is constructed firstly. The key influencing factors of the welded joints are obtained by means of NRSBCE algorithm subsequently. After that, the concept of fatigue characteristic domain is introduced according to the reduction result of the NRSBCE algorithm. Then, the master S-N curve fitting is carried out according to the divided fatigue characteristic domain. Correspondingly, a set of S-N curve clusters are fitted. Finally, fatigue analysis system for welded joints based on the fatigue characteristic domain is designed and developed. In the system, the equations of master S-N curve based on the mesh-insensitive structural stress method and the master S-N curve cluster based on the fatigue characteristic domain could be both obtained by uploading the practical fatigue test data of the aluminum alloy welded joints. This work helps to further reduce the dispersion degree of the fatigue sample data of the aluminum alloy welded joints thus provide reference for fatigue design.

The Design of Improved Neighborhood Rough Set Algorithm and Its Application in Diabetes Prediction Research

In order to reduce the importance of neighboring rough set only by single attribute, this paper proposes an improved neighborhood rough set attribute reduction algorithm (INRS), which increases the dependence of conditional attributes based on considering the importance of individual features. Relationship, that is, whether it affects the effect of other conditional attributes on decision attributes after deleting the attribute. The importance of a certain attribute to the decision attribute is divided into two parts: direct influence (after the attribute is deleted, the obtained decision attribute depends on the degree of reduction of the conditional attribute) and indirect influence (relative to the influence of other conditional attributes on the role of the decision attribute when there is no such attribute), so that the importance of each attribute can be clearly identified. When the attribute is reduced, the potential attributes are not reduced. In this paper, the data of diabetes in a hospital of the National Population and Health Science Data Sharing Service Platform was collected. The attribute reduction algorithm was used to attribute the diabetes dataset, and the random forest (RF) was used for classification prediction, which formed high precision. The Diabetes Prediction Model aims to provide support for doctors’ clinical diagnosis and disease research to improve the level of clinical diagnosis and treatment.

Power Electronics Technology Considering Rough Set Attribute Reduction Algorithm Application of Electric Braking in Large Power Grid

How to apply the advanced power electronic technology to the large power grid effectively, and improve the power electronic technology for meeeting the future actual development needs of the large power grid, which is also one of the main research tasks at present. In this paper, the actual demand of power electronic technology is discussed in detail in the process of the development of our country’s large power grid, and the actual development direction of power electronic technology in the process of the construction of large power grid is simply analyzed, and the main development goals in the later stage are further proposed.

A rough set-based bio-inspired fault diagnosis method for electrical substations

Imprecision and uncertainty in the alarm messages may significantly affect the accuracy and reliability of substation fault diagnosis results. To deal with that, a new rough set-based bio-inspired fault diagnosis method (RSBFDM) is proposed in this paper. It consists of four key components, namely the substation sub-region division method, the rough set attribute reduction algorithm, the binary reasoning spiking neural P system (BRSNPS), and the parallel reasoning algorithm. Specifically, the substation sub-region division method is used together with the rough set reduction algorithm to find the reduced fault production rule set for each sub-region. This simplifies the complexity of the problem and allows us to deal with fault alarm information uncertainty. Then, the BRSNPS and its reasoning algorithm are proposed to fulfill the fault knowledge representation and reasoning, yielding accurate fault diagnosis results. Thanks to the collaboration of rough sets and spiking neural P systems, no historical statistics and expertise are required and the scale of the problem is reduced. Experimental results carried out on realistic 110 kV and 750 kV substations show that the proposed method outperforms other alternatives.

Strip Surface Defects Recognition Based on PSO-RS&SOCP-SVM Algorithm

In order to improve the strip surface defect recognition and classification accuracy and efficiency, Rough Set (RS) attribute reduction algorithm based on Particle Swarm Optimization (PSO) algorithm was used on the optimal selection of strip surface defect image decision features, which removed redundant attributes, provided reduction data for the follow-up Support Vector Machine (SVM) model, reduced vector machine learning time, and constructed the SVM classifier, which uses Second-Order Cone Programming (SOCP) and multikernel Support Vector Machine classification model. Six kinds of typical defects such as rust, scratch, orange peel, bubble, surface crack, and rolled-in scale are recognized and classification is made using this classifier. The experimental results show that the classification accuracy of the proposed algorithm is 99.5%, which is higher than that of SVM algorithm and Relevance Vector Machine (RVM) algorithm. And because of using the Rough Set attribute reduction algorithm based on PSO algorithm, the learning time of SVM is reduced, and the average time of the classification and recognition model is 58.3 ms. In summary, the PSO-RS&SOCP-SVM evaluation model is not only more efficient in time, but also more worthy of popularization and application in the accuracy.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

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.

Breast cancer early diagnosis based on hybrid strategy.

The frequent occurrence of breast cancer and its serious consequences have attracted worldwide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an AdaBoost-SVM classification algorithm, combined with the cluster boundary sampling preprocessing techniques (CBS-AdaBoost-SVM), is proposed in this paper for the early diagnosis of breast cancer. The algorithm uses machine learning method to diagnose the unknown image data. Moreover, not all of the characteristics play positive roles for classification. To address this issue the paper delete redundant features by using Rough set attribute reduction algorithm based on the genetic algorithm (GA). The effectiveness of the proposed methods are examined on DDSM by calculating its accuracy, confusion matrix, and receiver operating characteristic curves, which give important clues to the physicians for early diagnosis of breast cancer.

The Application of Data Mining in the Civil Aviation Accident Analysis

At present, our analysis of the aviation accident mainly limited to the methods of mathematical statistics, the analysis method means of a single, and in a passive state, so the accident prediction is poor. This paper, basis on the rough set theory in data mining and preferential information ,we improve the rough set attribute reduction algorithm, and applied to civil aviation accident analysis to indentify the potential law of accident.

Analysis of Attribute Reduction of Rough Set and CNC Machine Fault Diagnosis Based on Particle Swarm

After reasoning and calculation, fault diagnosis can automatically identify the causes of malfunction based on the fault symptoms, which is the core task of fault diagnosis. This paper applies the particle swarm algorithm and rough set to the fault diagnosis, and proposes fault diagnosis knowledge acquisition, rules optimization and fault identification based on rough set attribute reduction of particle swarm. Firstly, this paper introduces the rough set attribute reduction. Secondly, the particle swarm algorithm is applied to the rough set attribute reduction algorithm. Finally, the correctness and superiority of this algorithm are proved from the reduction experimental results of related data sets.

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min–Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.