Attribute Reduction Research Articles

Attribute Reduction Method Based on Sequential Three-Branch Decision Model

Attribute Reduction Method Based on Sequential Three-Branch Decision Model

Effect of an algorithm for automatic placing of standardised test order sets on low-value appointments and attendance rates at four Spanish teaching hospitals: an interrupted time series analysis

ObjectiveReducing backlogs for elective care is a priority for healthcare systems. We conducted an interrupted time series analysis demonstrating the effect of an algorithm for placing automatic test order sets...

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Retracted: Attribute Reduction Algorithm on Concept Lattice and Application in Smart City Energy Consumption Analysis

Retracted: Attribute Reduction Algorithm on Concept Lattice and Application in Smart City Energy Consumption Analysis

Attribute reduction algorithms with an anti-noise mechanism for hybrid data based on fuzzy evidence theory

Attribute reduction can remove data noise and redundancy, thus reducing computational complexity, which is very important for machine learning. Because the difference between nominal attribute values is difficult to measure, attribute reduction for hybrid data faces challenges. In addition, most of the existing methods are sensitive to noise due to the lack of an anti-noise mechanism. Decision attribute contains the most important information of data. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a new fuzzy relation with an anti-noise mechanism is defined. Furthermore, fuzzy belief and fuzzy plausibility are defined based on the defined new distance and new fuzzy relation. In this framework, two anti-noise attribute reduction algorithms for hybrid data are proposed. Experiments on 12 data sets of various types show that compared with the other 8 state-of-the-art algorithms, the proposed algorithms improve the classification accuracy by at least 2% and the anti-noise ability by at least 11%. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.

Analysis of core attribute and approximate reduct based on the three-way decision

Attribute reduction plays an important role in pattern recognition and machine learning, and the theory of rough sets has become a commonly used model for attribute reduction since its superiority in describing and quantifying vagueness and uncertainty. However, little attention has been paid to analyzing the importance of core attributes in different circumstances, and core attributes are generally all selected into the reduct without considering their differences. In this study, the role of core attributes is analyzed in terms of their impacts on classification ability, and the three-way partition of attributes is proposed to distinguish different core attributes and condition attributes. Then, a unified approximate attribute reduction framework based on the three-way decision is introduced to keep the quality and resulting classification performance of the reduct. Moreover, a general forward-adding and back-deleting heuristic algorithm is developed to effectively select important attributes and also eliminate unimportant core and condition attributes in the boundary region. Comprehensive comparative experiments and statistical significance analysis are conducted on UCI data sets. The experimental results show that our method achieves a better attribute reduction rate and classification performance and also verify that core attributes are not always indispensable.

Machine learning inspired workflow to revise field development plan under uncertainty

We present an efficient and risk-informed closed-loop field development (CLFD) workflow for recurrently revising the field development plan (FDP) using the accrued information. To make the process practical, we integrated multiple concepts of machine learning, an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models. These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables. Unlike previous studies, we also added the execution time of the CLFD workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow. To appreciate the importance of data assimilation and new well-logs in a CLFD workflow, we carried out researches at rigorous conditions without a reduction in uncertainty attributes. The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models. The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty, despite working with a giant field with highly heterogeneous characteristics. It is demonstrated that the CLFD workflow can improve the efficiency by over 85% compared to the previously validated workflow. Finally, we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

Two-dimensional improved attribute reductions based on distance granulation and condition entropy in incomplete interval-valued decision systems

Attribute reductions rely on knowledge granulation and information measurement. Aiming at incomplete interval-valued decision systems (IIVDSs), an attribute reduction (with the FSR-AR/HS-AR algorithm) emerges by combining distance granulation and condition entropies; however, its distance measurement has defects for maximum and minimum fillings, while its condition entropy has promotion space on information enrichment. This paper utilizes FSR-AR/HS-AR to make the two-dimensional improvements of distance granulation and condition entropy to improve IIVDS-driven attribute reductions. Concretely, distance measures and similarity degrees are corrected via range completion and statistical enhancement, and the condition entropy is deepened by replacing credibility with coverage-credibility, so 2×2=4 reduction algorithms extend and improve FSR-AR/HS-AR. First, new maximal and minimal distances are defined via complete search and statistical optimization of missing values, so an amended fuzzy α-similarity relation induces knowledge granulation. Then, improved informational, conditional, joint entropies, and mutual information are established via coverage-credibility, and they obtain system equations and granulation nonmonotonicity. Furthermore, the two-dimensional improvements generate 2×2=4 attribute reductions, and the corresponding heuristic algorithms include FSR-AR/HS-AR and three improved algorithms. Finally, improvements in uncertainty measures and reduction algorithms are validated via table examples and data experiments, and the three novel algorithms outperform FSR-AR/HS-AR and contrastive algorithms for classification performances.

A NOVEL ALGORITHM FOR FINDING ALL REDUCTS IN THE INCOMPLETE DECISION TABLE

Attribute reduction, or attribute selection in the decision table, is a fundamental problem of rough set theory. Currently, many scientists are interested in and developing these issues. Unfortunately, most studies focus mainly on the complete decision table. On incomplete decision tables, researchers have proposed tolerance relations and designed attribute reduction algorithms based on different measures. However, these algorithms only return a reduct and do not preserve information in the decision tables. This paper will propose an efficient method to determine entire reducts of incomplete decision tables according to the relational database approach. In the complex case, this algorithm has exponential computational complexity. However, this algorithm has polynomial computational complexity in the different cases of databases.

A q-rung orthopair fuzzy multi-attribute group decision making model based on attribute reduction and evidential reasoning methodology

Multi-attribute group decision-making (MAGDM) is an analytical decision-making framework which gathers the views of multiple experts, and they often assess alternatives by degree adverbs in realistic scenarios. In order to treat expert opinions well, q-rung orthopair fuzzy sets (q-ROF) emerged as a data disposal tool, however, how to aggregate the information rationally remains a challenge. In this paper, q-ROF evidential reasoning methodology (q-ROF ERM) is proposed to guarantee the functionality in the case of conflicting, extreme information evaluations from experts and attributes. Second, the q-ROF attribute reduction (AR) framework is invented for attribute importance measurement. The flexibility and elegance of the model are boosted by the variation of the λ parameter and its lead to the elimination of redundant attributes. In addition to this, the q-ROF output three-way decision (TWD) framework is created to give the classification and ranking of alternatives. The relative output function derived from fuzzy distance measure and the adjustable utility pursuit coefficient τ afford both objectivity and subjectivity to the program selection, allowing the evaluation outcomes to be relevant to human cognition. Finally, the comparative experiments and sensitivity analyses of the two cases illustrate the scientificity and reliability of the presented framework in the MAGDM scenario.

A novel fuzzy-rough attribute reduction approach via local information entropy

Attribute reduction has become an essential challenge in the fields of pattern recognition, data mining and knowledge discovery. As a good indicator of the correlation between variables, information entropy has been widely used as a measure in several attribute reduction algorithms. Its calculation information only comes from the lower approximation, and other information in the calculation process is usually ignored. At the same time, due to the need to consider all objects when using traditional information entropy calculation, attribute reduction is time-consuming and may cause overfitting problems, so it is urgent to improve its computational efficiency and avoid overfitting. Therefore, a new information entropy, local information entropy, is defined. Experiments show that the local information entropy can further improve the computational efficiency of attribute reduction, and will not significantly decrease the classification precision.

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.

Interactive fuzzy knowledge distance-guided attribute reduction with three-way accelerator

Attribute reduction with an accelerator is an efficient strategy to handle large-scale information systems, but it is rarely suitable for information systems with fuzzy decision (ISFD). Meanwhile, both three-way approximations (TA) and fuzzy knowledge distance (FKD) are two types of attractive uncertainty measures in ISFD. Therefore, it prompts us to deliberate on how to interact the merits of TA with FKD to develop an attribute reduction with a three-way accelerator for ISFD. Driven by this concern, the TA of fuzzy decision is used as prior knowledge to interact with FKD, then a model named ITA-FKD is constructed to explore the monotonicity of uncertainty in multi-granularity spaces and measure the significance of every attribute. A three-way accelerator is implemented to eradicate redundant objects and attributes with the aid of the ITA-FKD model. Subsequently, a filter-wrapper approximate attribute reduction with the unique accelerator is successfully devised. Ultimately, some comprehensive experiments with high-dimensional datasets containing biomedicine field exemplified the higher efficiency of the explored algorithm.

Stream Convolution for Attribute Reduction of Concept Lattices

Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

Reduction in a fuzzy probability information system based on incomplete set-valued data

Attribute reduction for incomplete data is a hot topic in rough set theory (RST). A fuzzy probabilistic information system (FPIS) combines of fuzzy relations that satisfy the probability distribution about objects, which can be regarded as an information system (IS) with fuzzy relations. This paper studies attribute reduction in an FPIS. Based on the available information of objects on an ISVIS, the probability distribution formula of objects is first defined. Then, an FPIS can be induced by an ISVIS. Next, attribute reduction in a FPIS is proposed similar to an IS. Moreover, information granulation and information entropy in an FPIS is defined, and the corresponding algorithms are constructed. Finally, the effectiveness of the constructed algorithms is verified by k-means clustering, Friedman test and Nemenyi test.

Developing an Online Game-Based Index System for Identifying Economically Disadvantaged College Students using Hesitant Fuzzy Attribute Reduction

Developing an Online Game-Based Index System for Identifying Economically Disadvantaged College Students using Hesitant Fuzzy Attribute Reduction

EVALUATION OF SUGARCANE HYBRID CLONES ON WEED DYNAMICS, SUGAR QUALITY AND PLANT CROP PRODUCTIVITY AT BADEGGI, NIGERIA

Plant breeding programme develop varieties primarily to increase yield and resistance to diseases/insects with little emphasis on weeds dynamics. This study was conducted to evaluate sugarcane hybrid clones for weed dynamics, sugar quality and plant crop productivity at the Upland Sugarcane Experimental Field of National Cereals Research Institute, Badeggi, Nigeria. A total of sixteen clones were planted on a Randomized Complete Block Design (RCBD) with three replications. Analysis of variance revealed significant differences among the clones for some traits. Our results show that P. scrobiculatum (Linn.), S. pumila (Poir.), P. leucanthus (Schum & Thonn), B. diffusa (L), H. suaveolens (Poit), C. esculentus (Linn.) and K. squamulata (Thorn.ex Vahl) were the major weed problems of sugarcane at Badeggi. Lower weed dry matter (0.18 g m-2), and taller plants (194.07 cm), higher cane yield (136.8 t ha-1) and Sugar yield (12.93 t ha-1) were found in BD -1098-003 m among the studied hybrids. BD 1098-005 m, BD 441-007 m and B 47419 possess higher fibre, these materials can be utilized for energy cane variety. Brix percent also varied among the clones with increase in crop age, which can be used as indicator for maturity. The results revealed that some weed species types and their duration of infestation are among the numerous factors responsible for the reduction in growth and yield attributes of sugarcane crop. The promising hybrids will be advance for more evaluation at different locations for yield and juice quality stability studies.

Unsupervised Attribute Reduction Algorithm for Mixed Data Based on Fuzzy Optimal Approximation Set

Fuzzy rough set theory has been successfully applied to many attribute reduction methods, in which the lower approximation set plays a pivotal role. However, the definition of lower approximation used has ignored the information conveyed by the upper approximation and the boundary region. This oversight has resulted in an unreasonable relation representation of the target set. Despite the fact that scholars have proposed numerous enhancements to rough set models, such as the variable precision model, none have successfully resolved the issues inherent in the classical models. To address this limitation, this paper proposes an unsupervised attribute reduction algorithm for mixed data based on an improved optimal approximation set. Firstly, the theory of an improved optimal approximation set and its associated algorithm are proposed. Subsequently, we extend the classical theory of optimal approximation sets to fuzzy rough set theory, leading to the development of a fuzzy improved approximation set method. Finally, building on the proposed theory, we introduce a novel, fuzzy optimal approximation-set-based unsupervised attribute reduction algorithm (FOUAR). Comparative experiments conducted with all the proposed algorithms indicate the efficacy of FOUAR in selecting fewer attributes while maintaining and improving the performance of the machine learning algorithm. Furthermore, they highlight the advantage of the improved optimal approximation set algorithm, which offers higher similarity to the target set and provides a more concise expression.

Semi-supervised attribute reduction based on label distribution and label irrelevance

Attribute reduction in partially labeled data, also called semi-supervised attribute reduction, is an important issue. In recent years, the research on semi-supervised attribute reduction has attracted the attention of many scholars. Unfortunately, most existing semi-supervised attribute reduction methods do not handle the information loss caused by missing labels well. Meanwhile, these methods in general only consider the relevance between attributes and labels to measure attribute correlations, which ignores the irrelevant information contained in the attributes with respect to the labels. In view of this, this paper proposes a novel semi-supervised attribute reduction algorithm considering attribute relevance, redundancy and label irrelevance from the perspective of label distribution. Firstly, the membership degree of unlabeled objects relative to labels is defined by fuzzy similarity relation, which implements information restoration and converts partially labeled data into label distribution data. Secondly, some fuzzy uncertainty measures for label distribution are defined and related properties are investigated accordingly. Additionally, considering that irrelevant information brought by attributes may lead to over-fitting, label irrelevance criterion based on fuzzy uncertainty measures is constructed. Thirdly, a novel semi-supervised attribute reduction algorithm via the maximum relevance, minimum redundancy, and minimum irrelevance is proposed. Finally, compared with the representative semi-supervised attribute reduction algorithms and supervised attribute reduction algorithm, the effectiveness of the proposed algorithm is verified by various experiments.

Exploring salt tolerance in Indian mustard [Brassica juncea (L.) Czern. & Coss.] genotypes: A physiological and biochemical perspective

Salinity is a crucial problem affecting the productivity of crops across the globe and poses a serious threat to agricultural food production and food security. Salinity stress adversely affects the growth and development of plants by reducing growth attributes, chlorophyll content, and relative water content and further, led to nutritional toxicities and high osmotic stress. The present investigation was employed to assess the response of ten mustard [Brassica juncea (L.) Czern. & Coss.] genotypes under different regimes of salt concentrations (0, 50, 100, 150, 200, 300 mM NaCl) for tolerance potential using principal component analysis (PCA). The PCA analysis of studied attributes found that Pusa Jai Kisan was salt-tolerant and Pusa Bold was susceptible under different salt conditions compared to the rest of the mustard genotypes. The reduction in morphological attributes, the maximum quantum yield of PSII (Fv/Fm), total chlorophyll, and relative water content was lower in Pusa Jai Kisan and higher in Pusa Bold with increasing concentrations of salinity. In addition, the accumulation of Na+ and Cl‒ ions was increased with increasing salinity stress. Based on growth performance, ten genotypes of mustard were ranked depending on their tolerance to salinity stress: Pusa Jai Kisan > Pusa Mustard 30 > Pusa Jagannath > Pusa Mustard 28 > Pusa Vijay > Pusa Tarak > Pusa Mustard 25 > Pusa Mustard 27 > Pusa Mahak > Pusa Bold, respectively. Thus, PCA analysis can be used as a powerful tool for the identification of tolerant and susceptible genotypes under salinity stress. The understanding of mustard responses towards salinity stress could help in the selection of tolerant genotypes and this selection may play a crucial role in further improving the effectiveness of breeding.