Attribute Reduction Model Research Articles

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

Kernel multi-granularity double-quantitative rough set based on ensemble empirical mode decomposition: Application to stock price trends prediction

As financial markets grow increasingly complex and dynamic, accurately predicting stock price trends becomes crucial for investors and financial analysts. Effectively identifying and selecting the most predictive attributes has become a challenge in stock trends prediction. To address this problem, this study proposes a new attribute reduction model. A rough set theory model is built by simplifying the prediction process and combining it with the long short-term memory network (LSTM) to enhance the accuracy of stock trends prediction. Firstly, the Ensemble Empirical Mode Decomposition (EEMD) is utilized to decompose the stock price data into a multi-granularity information system. Secondly, due to the numerical characteristics of stock data, a kernel function is applied to construct binary relationships. Thirdly, recognizing the noise inherent in stock data, the double-quantitative rough set theory is utilized to improve fault tolerance during the construction of decision attributes' lower and upper approximations. Moreover, calculate the correlation between conditional and decision attributes, and retain highly correlated conditional attributes for prediction. The kernel multi-granularity double-quantitative rough set based on the EEMD (EEMD-KMGDQRS) model proposed identifies the key factors behind stock data. Finally, the efficacy of the proposed model is validated by selecting 356 stocks from diverse industries in the Shanghai and Shenzhen stock markets as experimental samples. The results show that the proposed model improves the generalization of attribute reduction results through a fault tolerance mechanism by combining kernel function with multi-granularity double-quantitative rough set, thereby enhancing the accuracy of stock trends prediction in subsequent LSTM prediction processes.

Attribute reduction for incomplete mixed data based on neighborhood information system

In an era of data-based and information-centric Industry 4.0, extracting potential knowledge and valuable information from data is central to data mining tasks. Yet, the ambiguity, imprecision, incompleteness, and hybrid in real-world data pose tremendous challenges to critical information mining. Accordingly, we propose a new Max-Correlation Min-Redundant (MCMR) attribute reduction model from the uncertainty relation of attributes to avoid information loss in incomplete mixed data. Specifically, the neighbor relations are primarily developed based on the soft computing approach of the neighborhood information system, which divides the objects into neighborhood covers to maximize the utilization of the information in the incomplete mixed data. Then, we detailly analyze the internal and external consistency relationships of the four main uncertainty functions. Based on this, a new MCMR uncertain function is designed with maximum relevance and minimum redundancy. Experiments on nine real-world datasets validate the proposed model can improve data quality by mining critical information in classification tasks and achieving optimal performance with a minimum number of attributes.

A robust approach to attribute reduction based on double fuzzy consistency measure

Attribute reduction with fuzzy rough sets is to obtain a compact and informative attribute subset from the original attribute set, in which the construction of the attribute evaluation function is a crucial issue. The existing evaluation functions are mainly based on the lower approximation, which ignore the discernment samples provided by the upper approximation or boundary information characterized by the two approximations. It is necessary to simultaneously mine the valuable information in upper and lower approximations from the absolute quantitative perspective and in the boundary from the relative quantitative perspective, which is helpful to comprehensively characterize the consistency and difference between attributes and decisions. In this paper, we first propose a double fuzzy consistency measure (DFCM) based on the two kinds of information described by fuzzy upper and lower approximations to evaluate the importance of attributes for learning tasks. By the mutual proximity of two approximations with the gradual increase of attributes, the measure DFCM is monotonically decreasing to attributes, which can be used to describe the consistency and difference between conditional and decision attributes. Meanwhile, attribute reduction model and algorithm based on the measure are developed. Finally, the detailed experimental comparisons indicate that the double fuzzy consistency measure based attribute reduction method can achieve effective and robust classification performance by grabbing fewer attributes with discernment information.

Unsupervised attribute reduction for mixed data based on fuzzy rough sets

Unsupervised attribute reduction becomes very challenging due to a lack of decision information, which is to select a subset of attributes that can maintain learning ability without decision information. However, most of the existing unsupervised attribute reduction methods are proposed for numerical or nominal attributes, and little research has been done on unsupervised mixed attribute reduction methods. In view of this, this paper proposes a generalized unsupervised mixed attribute reduction model based on fuzzy rough sets . First, based on all single attribute subsets , the significance is defined to indicate the importance of a candidate attribute. Then, a specific fuzzy rough-based unsupervised attribute reduction (FRUAR) algorithm is designed. Finally, the proposed algorithm is compared with the existing algorithms by using thirty public data sets. Experimental results show that the algorithm FRUAR can select fewer attributes to maintain or improve the performance of learning algorithms, and it is suitable for mixed attribute data.

Multiattribute Fuzzy Decision Evaluation Approach and Its Application in Enterprise Competitiveness Evaluation

Multiattribute decision-making approach is one of the key complex system evaluation technologies which has attracted high attention of academic research studies. This paper establishes a novel multiattribute decision evaluation approach. First, we propose a high-dimensional data attribute reduction model based on partial correlation analysis and factor analysis methods. Second, based on the attribute weights calculated by multiple weighting methods, the corresponding multiple evaluation score vectors of the objects evaluated can be obtained. The final scoring vector can be determined by combining the quadratic combination weighting and the Spearman consistency test. Third, we use fuzzy C-means algorithm to grade evaluated objects. Finally, the established evaluation approach in this paper is verified by using the 107 observations in China. This approach also provides a decision-making example for attribute reduction of high-dimensional data, scoring of complex system evaluation, and clustering analysis when conducting evaluation in other fields.

Neighborhood rough set-based three-way clustering considering attribute correlations: An approach to classification of potential gout groups

Using modern information theory to classify and identify high-risk disease groups is one of the research concerns in medical decision-making. The early diagnosis of gout is missing a single indicator, and relying on artificial labeling of disease characteristics is not only costly for decision-making, but also has a high misdiagnosis rate. Aiming at incomplete and attribute-related random large sample data, we propose a three-way clustering algorithm based on neighborhood rough sets, which is used to initially label the data, reduce the rate of misdiagnosis, and improve decision-making efficiency. Firstly, a neighborhood rough set theory in a heterogeneous information system is established. Secondly, the Best-Worst method-based neighborhood rough set attribute reduction model considering attribute correlation is constructed. Thirdly, a neighborhood rough set-based three-way clustering method for heterogeneous information system is proposed. Finally, we use 2,683 random samples and the proposed model to identify and classify potential gout patients in the samples. The results show that the proposed model can be used to mark and cluster potential gout groups in random samples without prior probability and with fuzzy decision rules, which is helpful for clinical decision-making.

A high-dimensional attribute reduction method modeling and evaluation based on green economy data: evidence from 15 sub-provincial cities in China

Data play an increasingly crucial role in decision evaluation. However, the noise and redundant information in the data create confusion to the decision makers. To solve this problem, this paper creates a new attribute reduction model based on technique for order preference by similarity to an ideal solution (TOPSIS), grey correlation analysis and coefficient of variation approaches. First, we obtain the time weights of panel data in different years by TOPSIS and then transfer the panel data into a cross-sectional data matrix. Second, we delete the overlapping attributes by grey correlation analysis method. Third, we use the coefficient of variation to select the attributes with the highest information content. Finally, the proposed attribute reduction model has been varied based on the green economy evaluation data of 15 sub-provincial cities in China. The experimental findings provide decision-making reference for the local government policymakers to adjust or formulate green economic development strategies.

Attribute reduction based on the Boolean matrix

Attribute reduction is an important preprocessing step in machine learning and pattern recognition. This paper introduces condition-attribute Boolean matrix and decision-attribute Boolean matrix and proposes a new attribute reduction model based on Boolean operations according to the concept of neighborhood rough set. Some operation rules of Boolean vectors are defined and an uncertainty measure, named attribute support function, is proposed to evaluate the importance degree of candidate attributes with respect to decision attributes. An attribute reduction algorithm based on the proposed measure is designed. Ten data sets selected from public data sources are used to compare the proposed algorithm with some existing algorithms. The experimental results show that the proposed reduction algorithm is feasible and effective.

Attribute reduction of relative knowledge granularity in intuitionistic fuzzy ordered decision table

For the moment, the attribute reduction algorithm of relative knowledge granularity is very important research areas. It provides a new viewpoint to simplify feature set. Based on the decision information is unchanged, fast and accurate deletion of redundant attributes, which is the meaning of attribute reduction. Distinguishing ability of attribute sets can be well described by relative knowledge granularity in domain. Therefore, how to use the information based on relative knowledge granularity to simplify the calculation of attribute reduction. It is an important direction of research. For increasing productiveness and accuracy of attribute reduction, in this paper we investigate attribute reduction method of relative knowledge granularity in intuitionistic fuzzy ordered decision table(IFODT). More precisely, we redefine the granularity of knowledge and the relative knowledge granularity by ordered relation. And their relevant properties are proved. On the premise that the decision results remain unchanged, in order to accurately calculate the relative importance of any condition attributes about the decision attribute sets, the conditional attribute of internal and external significance are designed by relative knowledge granularity. And some important properties of relative attribute significance are proved. Therefore, we determine the importance of conditional attributes based on the size of the relative attribute significance. In the aspect of computation, the corresponding algorithm is designed and time complexity of algorithm is calculated. Moreover, the attribute reduction model of relative knowledge granularity of efficiency and accuracy is proved by test. Last, the validity of algorithm is demonstrated by an case about IFODT.

Mechanisms and new parameter attribute reduction of high-speed railway wheel rim steel subjected to low temperature fatigue

A series of characterization tests were performed to elucidate the high-cycle fatigue behavior in a type of steel often used in high-speed railway wheel rims. The specimens were cyclically-loaded with a constant stress amplitude of 370MPa at the stress ratios of −1 at a test temperature range from −60°C to 60°C. It was found that the steel showed differences in the evolution of microstructures (slip system, dislocation structure, austenite transformation, sub-grains), which led to significant changes in the mechanism of dissipation of strain and fatigue behavior. However, all microstructure orientations on fracture surfaces within grains or sub-grains were approximately uniform and were inclined to primary slip planes {110} and {112} in BCC crystal structure. In addition, the rough set theory model was introduced for the attribute reduction of characteristic parameters. Four sets of attribute reductions based on decisions were obtained, and each one had only three characteristic parameters. Developing a new parameter attribute reduction model was of great importance in building a comprehensive understanding of the characteristic parameters as well as the development of new methods for reliable fatigue lifetime calculations.

Port throughput influence factors based on neighborhood rough sets: An exploratory study

Purpose: The purpose of this paper is to devise a efficient method for the importance analysis on Port Throughput Influence Factors. Design/methodology/approach: Neighborhood rough sets is applied to solve the problem of selection factors. First the throughput index system is established. Then, we build the attribute reduction model using the updated numerical attribute to reduction algorithm based on neighborhood rough sets. We optimized the algorithm in order to achieve high efficiency performance. Finally, the article do empirical validation using Guangzhou Port throughput and influencing factors’ historical data of year 2000 to 2013. Findings: Through the model and algorithm, port enterprises can identify the importance of port throughput factors. It can provide support for their decisions. Research limitations: The empirical data are historical data of year 2000 to 2013. The amount of data is small. Practical implications: The results provide support for port business investment, decisions and risk control, and also provide assistance for port enterprises’ or other researchers’ throughput forecasting. Originality/value: In this paper, we establish a throughput index system, and optimize the algorithm for efficiency performance.

Multi-view attribute reduction model for traffic bottleneck analysis

In the field of traffic bottleneck analysis, it is expected to discover traffic congestion patterns from the reports of road conditions. However, data patterns mined by existing KDD algorithms may not coincide with the real application requirements. Different from academic researchers, traffic management officers do not pursue the most frequent patterns but always hold multiple views for mining task to facilitate traffic planning. They expect to study the correlation between traffic congestion and various kinds of road properties, especially the road properties easily to be improved. In this multi-view analysis, each view actually denotes a kind of user preference of road properties. Thus it is required to integrate user-defined attribute preferences into pattern mining process. To tackle this problem, we propose a multi-view attribute reduction model to discover the patterns of user interests. In this model, user views are expressed with attribute preferences and formally represented by attribute orders. Based on this, we implement a workflow of multi-view traffic bottleneck analysis, which consists of data preprocessing, preference representation and congestion pattern mining. We validate our approach based on the reports of road conditions from Shanghai. Experimental results show that the resultant multi-view mining outcomes are effective for analyzing congestion causes and traffic management.