Attribute Reduction Research Articles

Indistinguishable Element-Pair Attribute Reduction and Its Incremental Approach

Attribute reduction is a popular approach of preprocessing data. Discernibility matrix is a typical method that focuses on attribute reduction. Faced with the processing of modern information systems with large amounts of data and rapid changes, the traditional static discernibility matrix reduction model is powerless. To overcome this shortcoming, this paper first proposes an indistinguishable element pair method that does not need to store discernibility information, which retains the advantages of institution and easy-to-understand, and at the same time effectively solves the problem of space consumption. In order to make the model adapt to the processing of dynamic data sets, we further study the incremental mechanism and design a set of dynamic reduction models, which can adjust the reduction set in time according to the changes of objects. Theoretical analysis and experimental results indicate that the proposed algorithm is obviously superior to the discernibility matrix model, and can effectively deal with the reduction of dynamic data sets.

AN EFFECTIVE ALGORITHM FOR COMPUTING REDUCTS IN DECISION TABLES

Attribute reduction is one important part researched in rough set theory. A reduct from a decision table is a minimal subset of the conditional attributes which provide the same information for classification purposes as the entire set of available attributes. The classification task for the high dimensional decision table could be solved faster if a reduct, instead of the original whole set of attributes, is used. In this paper, we propose a reduct computing algorithm using attribute clustering. The proposed algorithm works in three main stages. In the first stage, irrelevant attributes are eliminated. In the second stage relevant attributes are divided into appropriately selected number of clusters by Partitioning Around Medoids (PAM) clustering method integrated with a special metric in attribute space which is the normalized variation of information. In the third stage, the representative attribute from each cluster is selected that is the most class-related. The selected attributes form the approximate reduct. The proposed algorithm is implemented and experimented. The experimental results show that the proposed algorithm is capable of computing approximate reduct with small size and high classification accuracy, when the number of clusters used to group the attributes is appropriately selected.

Attribute reduction with personalized information granularity of nearest mutual neighbors

Neighborhood-based attribute reduction plays a vital role in pattern recognition, for selecting a series of informative and relevant attributes from data sets. The increase in dimensionality and complexity of data along with superfluous and noisy attributes, results in significant and tricky issues when reasoning data. To address the ineffectiveness of classical attribute reduction within the neighborhood framework, a competent attribute reduction scheme based on a novel concept of nearest mutual neighbor-based personalized information granularity is proposed in this paper. This study comprises three phases. First, we intend to integrate the strategies of personalized radius and two-sided neighbors into the modeling of information granularity, and both personalized and mutual neighborhood rough sets are developed. In the second phase, the concept of the belief function from evidence theory is introduced to construct an adaptive neighborhood information granule with the guidance of the principle of justifiable granularity; in this sense, the search for a justifiable granularity is transformed into an optimization issue. In the third phase, a new neighborhood homogeneous evidence-based attribute significance evaluation is discussed to avoid the inadequacy of voting-based attribute measures. In addition, an enhanced forward greedy algorithm is proposed to address non-monotonic circumstances. For the experimental evaluation, fifteen benchmark data sets, involving six high-dimensional microarray data sets, are chosen to display the performance of the method and algorithm. The experimental findings reveal the superiority of our work from different perspectives.

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.

Attribute Reduction Based on Lift and Random Sampling

As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to avoid overfitting, uncontrollable sampling may still affect the efficiency of search reduct. By utilizing inherent characteristics of each label, Multi-label learning with Label specIfic FeaTures (Lift) algorithm can improve the performance of mathematical modeling. Therefore, here, it is attempted to use Lift algorithm to guide the sampling for reduce the uncontrollability of sampling. In this paper, an attribute reduction algorithm based on Lift and random sampling called ARLRS is proposed, which aims to improve the efficiency of searching reduct. Firstly, Lift algorithm is used to choose the samples from the dataset as the members of the first group, then the reduct of the first group is calculated. Secondly, random sampling strategy is used to divide the rest of samples into groups which have symmetry structure. Finally, the reducts are calculated group-by-group, which is guided by the maintenance of the reducts’ classification performance. Comparing with other 5 attribute reduction strategies based on rough set theory over 17 University of California Irvine (UCI) datasets, experimental results show that: (1) ARLRS algorithm can significantly reduce the time consumption of searching reduct; (2) the reduct derived from ARLRS algorithm can provide satisfying performance in classification tasks.

Determining Attributes for Discrete Choice Experiments in Consumer Research

Attribute development for Discrete Choice Experiments (DCEs) through qualitative research is a shallowly covered area. Particularly, research is scant in terms of detail about attribute reduction and development. First of all, the paper reviews the literature and justify DCEs as an appropriate method for determining consumer preferences, with particular focus on services. The researcher conducted a DCE on preferences for restaurants for leisure purposes. Whilst finding there was not a clear guidance, the author proposes a clear methodology for guiding DCE researchers regarding the determination of attributes in consumer research, particularly in the service industry. The case study of restaurant attribute development is used. In this context, the first stage consisted in a thorough review of the literature combined with a thematic analysis of six focus group interviews conducted in the South East of the UK. Attribute reduction took place through constant comparison analysis and the method is comprehensively explained in the paper. The study also looked for control variables and found that occasion for eating out is a very salient variable to be tested in the following DCE which consisted in an Adaptive Choice Based Conjoint online survey (Sawtooth Software). It was found the that combining literature review with focus group interviews results in richer attributes because they are based on more complex and nuanced data. Finally, a proposal with nine steps for attribute development is provided.

Eugenol, Isolated from the Essential Oil from Lonicera japonica Flower Buds, Could Increase the Oxidative Stability of Sunflower Oil in the Deep-Frying Procedure of Youtiao

In order to assess the sunflower oil (SFO) oxidative stability that was added by the essential oils extracted from Lonicera japonica flower buds (LJEO) during deep-frying at 180 °C for 30 h, we clarified the compound/compounds of LJEO that improved the oxidative stability of SFO. The results displayed that the addition of LJEO (0.06 g/kg) could significantly restrict the elevation or the reduction in the levels of total polar compounds (TPC), thiobarbituric acid (TBA), conjugated dienes and conjugated trienes, and the values for polymer, viscosity and the color of SFO during the whole period. Meanwhile, the reduction in the sensory attributes, including flavor, taste, crispness and overall acceptability of the fried product, youtiao, was obviously restricted as well. After the bioassay-guided fractionation of LJEO and repeated deep-frying at 180 °C for 30 h, one of its chemical constituents, eugenol, was demonstrated to be the very compound that did significantly inhibit the oxidative rancidity of the SFO. Therefore, eugenol may be employed as potential effective natural antioxidants to inhibit the oxidative rancidity of SFO during its deep-frying procedures.

MapReduce accelerated attribute reduction based on neighborhood entropy with Apache Spark

Attribute reduction is nowadays an extremely important data preprocessing technique in the field of data mining, which has gained much attention due to its ability to provide better generalization performance and learning speed for analysis model. Rough set theory offers a systematic powerful framework for attribute reduction in terms of classificatory and decision abilities under uncertainty. In this paper, we present a parallel neighborhood entropy-based attribute reduction method with neighborhood rough sets that uses the Apache Spark cluster computing model to realize the parallelization of algorithm in a distributed computing environment. In leveraging the horizontal partitioning strategy to alleviate the task of data parallelism, three quantitative measures of attribute sets, i.e., neighborhood approximation accuracy, neighborhood credibility and coverage degrees are parallelized to accelerate the computation of decision neighborhood entropy during the heuristic search iterative process. A novel parallel heuristic attribute reduction algorithm is then developed by employing several operations from Spark API to ease the code parallelization. Extensive experimental results indicate the superiority and notable strengths of the proposed algorithm in terms of the criteria for evaluating parallel performance, i.e., scalability and extensibility.

Parallel incremental efficient attribute reduction algorithm based on attribute tree

Attribute reduction is an important application of rough sets. Efficiently reducing massive dynamic data sets quickly has always been a major goal of researchers. Traditional incremental methods focus on reduction by updated approximations. However, these methods must evaluate all attributes and repeatedly calculate their importance. When these algorithms are applied to large datasets with high time complexity, reducing large decision systems becomes inefficient. We propose an incremental acceleration strategy based on attribute trees to solve this problem. The key step is to cluster all attributes into multiple trees for incremental attribute evaluation. Specifically, we first select the appropriate attribute tree for attribute evaluation according to the attribute tree correlation measure to reduce the time complexity. Next, the branch coefficient is added to the stop criterion, increasing with the branch depth and guiding a jump out of the loop after reaching the maximum threshold. This avoids redundant calculation and improves efficiency. Furthermore, we propose an algorithm for incremental attribute reduction based on attribute trees using these improvements. Finally, a Spark parallel mechanism is added to parallelize data processing to implement the parallel incremental efficient attribute reduction based on the attribute tree. Experimental results on the Shuttle dataset show that the time consumption of our algorithm is more than 40% lower than that of the classical IARC algorithm while maintaining its good classification performance. In addition, the time is shortened by more than 87% from the benchmark after adding the Spark parallelizing mechanism.

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.

Purple stem Brassica napus exhibits higher photosynthetic efficiency, antioxidant potential and anthocyanin biosynthesis related genes expression against drought stress.

Purple-stem Brassica napus (B. napus) is a phenotype with unique color because of its high anthocyanins content. Anthocyanins are naturally occurring plant pigments that have antioxidants activity and play important role in plant defense against abiotic and biotic stresses. In the present study, drought induced effects on plants were investigated in hydroponically grown seedlings of green stem (GS) and purple stem (PS) genotypes of B. napus. The results of this study showed that the major function of anthocyanins accumulation during drought was to enhance the antioxidant capability and stress tolerance in B. napus plants. Our results showed that drought significantly inhibited the plant growth in terms of decreased biomass accumulation in both genotypes, although marked decline was observed in GS genotype. The reduction in photosynthetic attributes was more noticeable in the GS genotype, whereas the PS genotype showed better performance under drought stress. Under stressful conditions, both the genotype showed excessive accumulation of reactive oxygen species (ROS) and malondialdehyde (MDA), as well as higher levels of antioxidant enzymes activities. Under drought conditions, the GS genotype showed apparent damages on chloroplast deformation like in thylakoid membrane and grana structural distortion and fewer starch grains and bigger plastoglobuli. Moreover, during drought stress, the PS genotype exhibited maximum expression levels of anthocyanins biosynthesis genes and antioxidant enzymes accompanied by higher stress tolerance relative to GS genotype. Based on these findings, it can be concluded that GS genotype found more sensitive to drought stress than the PS genotype. Furthermore this research paper also provides practical guidance for plant biologists who are developing stress-tolerant crops by using anthocyanin biosynthesis or regulatory genes.

Attribute reduction in intuitionistic fuzzy formal concepts

Knowledge acquisition in intuitionistic fuzzy information systems is of importance because those fuzzy information systems are often encountered in many real-life problems. Formal concept analysis is a simple and effective tool for knowledge acquisition. However, there is still little work on introducing knowledge acquisition methods based on formal concept analysis into intuitionistic fuzzy information systems. This paper mainly extends the formal concept theory into intuitionistic fuzzy information systems. Firstly, two pairs of adjoint mappings are defined in intuitionistic fuzzy formal contexts. It is verified that both pairs of adjoint mappings form Galois connections. Secondly, two types of intuitionistic fuzzy concept lattices are constructed. After that, we also present the main theorems and propositions of the intuitionistic fuzzy concept lattices. Thirdly, we deeply discuss the attribute characteristics for type-1 generalized one-sided intuitionistic fuzzy concept lattice. Furthermore, a discernibility matrix-based algorithm is proposed for attribute reduction and the effectiveness of this algorithm is demonstrated by a practical example. The construction of intuitionistic fuzzy conceptS is meaningful for the complex and fuzzy information in real life.

On the Problem Related to Reductive Attributes in the Incomplete Decision Tables

Attribute reduction is a key problem in the process of data mining and knowledge discovery. Up to now, many attribute reduction algorithms in incomplete decision tables have been proposed. However, the research results related to conditional attributes and reduct of incomplete decision tables are still limited. By relational database approach, this paper investigates some properties of conditional attributes and proposes an algorithm to determine all reductive attributes of consistent incomplete decision tables in polynomial time. The proposed algorithm is an effective tool to eliminate all redundant attributes in data pre-processing in order to improve the efficiency of data mining models.

Attribute reduction for set-valued data based on D–S evidence theory

It is more difficult to manipulate a set-valued decision information system (SVDIS) than common information system due to its strong uncertainty. D–S evidence theory can accurately express the unknowness and uncertainty of knowledge and information, so it has a strong ability to deal with uncertainty. This paper studies the measurement of uncertainty based on D–S evidence theory in an SVDIS. First, a novel pseudo-distance between two objects in an SVDIS considering decision attributes is proposed, Second, the tolerance relation based on the pseudo-distance is established. And then, the belief and plausibility are defined on the basis of the tolerance relation. Experimental and statistical analysis show that the defined belief and plausibility work well in measuring the uncertainty of an SVDIS. Furthermore, λ-belief, λ-belief significance, λ-plausibility and λ-plausibility significance reduction algorithms based on the defined belief and plausibility are proposed and the equivalence between λ-belief reduction algorithm and λ-plausibility reduction algorithm is proved. Experimental results and statistical tests on six data sets with missing values from UCI show that the proposed reduction algorithms are statistically superior to some state-of-the-art algorithms in classification accuracy. These findings will provide a wider perspective on the uncertainty of an SVDIS.

Effects of 1-methylcyclopropene treatment on fruit quality and antioxidant activities of ruby-S apples during cold storage

The effects of 1-methylcyclopropene (1-MCP) on the fruit quality and antioxidant activity of ruby-S apples were evaluated during cold storage. The fruits were treated with 0 (control) and 1 μL/L 1-MCP and stored at 0°C for six months. The assessments were performed at 0 (harvest), 3, and 6 months of storage. Compared with the control, the 1-MCP-treated fruits exhibited a reduced internal ethylene concentration and slowed appearance of greasiness on the fruit skin. In addition, 1-MCP treatment helped maintain a reduction in flesh firmness, titratable acidity, and peel color attributes (L* and b*) of the fruit. However, 1-MCP treatment did not affect fruit weight loss, soluble solids content, or peel color a* value. The 1-MCP treatment helped delay the reduction in total phenolics (TPC) and total flavonoids (TFC) content as well as total antioxidant determined by 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity (RSA) in the peel tissue. In the pulp tissue, 1-MCP affected only TPC, and not TFC or DPPH RSA. Overall, this study suggests that 1-MCP treatment helps maintain the desirable fruit quality attributes of ruby-S apples during ripening, but its effect on antioxidant activity depends on the tissue type (peel and pulp) of the fruit.

Attribute reduction based on D-S evidence theory in a hybrid information system

As an important uncertainty reasoning method, Dempster-Shafer (D-S) evidence theory has been widely applied to expert system, comprehensive evaluation, information fusion and decision analysis. However, it has not been fully valued in uncertainty measurement. A hybrid information system (HIS) means an information system that contains many types of attributes (e.g., categorical attribute, real-valued attribute and attribute with missing values, etc.). It is more difficult to measure an HIS than an ordinary IS. This paper studies the use of evidence theory to measure the uncertainty of an HIS. Firstly, a novel distance between two objects in an HIS considering decision attributes is constructed, and secondly, the tolerance relation based on the constructed distance is established in an HIS. And then, belief and plausibility functions are defined by the tolerance relation. Furthermore, several algorithms for attribute reduction are designed on the basis of the defined belief and plausibility functions. In addition, we come to a series of conclusions on the relation among θ-reduction by using decision attributes, θ-belief reduction and θ-plausibility reduction, which further confirms the effectiveness of the designed attribute reduction algorithms. Finally, the experimental results and statistical test show that the defined belief and plausibility functions work well in measuring the uncertainty of an HIS and the designed reduction algorithm is superior to several state-of-the-art algorithms in classification accuracy. These results will provide a wider perspective on the uncertainty of an HIS.

Energy Saving via a Minimal Structure

Not only mathematical statistics, differential equations, and mathematical models were used to analyze and reduce data, but a rough set model is also employed in medical, engineering, and other fields to analyze and reduce them. The goal of this paper is to introduce a minimal structure concept to produce new rough set models and show that it is suitable for analyzing most real-life problems, reduction of attributes, and decision making. We examine the effectiveness of the following method in the problem of electric power generators and decision making. We also offer a comparison of our method and Pawlak’s method. Finally, the variable precision model improves the accuracy of decision making.

Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.

Attribute reduction based on observational consistency in intuitionistic fuzzy multi-covering decision systems

Attribute reduction is an important issue in data mining, machine learning and other applications of big data processing. Covering-based rough set and intuitionistic fuzzy (IF) set models are both the effective theoretical tools of uncertainty or imprecise computation, and thus IF covering rough set model has been acknowledged as a positive approach to attribute reduction. Based on IF covering rough set model, this study explores a kind of parameterized IF observational consistency in IF multi-covering decision system, and proposes an attribute reduction method. This article firstly defines the concepts of regular IF β-covering, parameterized IF observational sets on the regular IF β-covering approximation space. Secondly, the parameterized IF observational consistency is defined to be the principal of attribute reduction in the IF multi-covering decision system, and the related IF discernibility matrix is developed to provide a way of attribute reduction. For multi-observational consistency corresponding to an observational parameters set, an unified multi-observational discernibility matrix is constructed, which avoids the disadvantage of needing to construct multiple corresponding discernibility matrices separately. Furthermore, an attribute reduction algorithm based on iterative dissolving of unified multi-observational discernibility matrix is proposed, and the experiment to demonstrate effectiveness of algorithm is presented. Experiments with UCI datasets shows that, the proposed method is a good way for improving both the rates of attribute-reduced and the classification accuracy of reduced datasets.

Information entropy based attribute reduction for incomplete heterogeneous data

Attribute reduction in an information system (IS) is an important research topic in rough set theory (RST). This paper investigates attribute reduction for incomplete heterogeneous data based on information entropy. Information entropy in an incomplete IS with heterogeneous data (IISH) is first defined. Then, some derived notions of information entropy, such as joint information entropy, conditional information entropy, mutual information entropy, gain and gain ratio in an incomplete decision IS with heterogeneous data (IDISH), are presented. Next, information entropy is applied to perform attribute reduction in an IDISH. Two attribute reduction algorithms, based on gain and gain ratio, respectively, are proposed. Finally, in order to illustrate the feasibility and efficiency of the proposed algorithms, experimental analysis is carried out and comparisons are done. It is worth mentioning that the incomplete rate is used to deal with incomplete heterogeneous data.