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For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy and high cardinality. In this paper, a novel attribute reduction method using Lebesgue and entropy measures in neighborhood rough sets is proposed, which has the ability of dealing with continuous numerical data whilst maintaining the original classification information. First, Fisher score method is employed to eliminate irrelevant attributes to significantly reduce computation complexity for high-dimensional data sets. Then, Lebesgue measure is introduced into neighborhood rough sets to investigate uncertainty measure. In order to analyze the uncertainty and noisy of neighborhood decision systems well, based on Lebesgue and entropy measures, some neighborhood entropy-based uncertainty measures are presented, and by combining algebra view with information view in neighborhood rough sets, a neighborhood roughness joint entropy is developed in neighborhood decision systems. Moreover, some of their properties are derived and the relationships are established, which help to understand the essence of knowledge and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is designed to improve the classification performance of large-scale complex data. The experimental results under an instance and several public data sets show that the proposed method is very effective for selecting the most relevant attributes with high classification accuracy.
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12Distance metric learning-based multi-granularity neighborhood rough sets for attribute reduction
Distance metric learning-based multi-granularity neighborhood rough sets for attribute reduction
17Dominance-Based Neighborhood Rough Sets and Its Attribute Reduction
In real-life applications, partial order may exist in the domain of attributes and different data types often coexist in an decision system. Dominance-based rough set approach has been used widely in multi-attribute and multi-criteria decision by using the dominating relation. Neighborhood rough set aims to deal with hybrid data types. But the preference relation in the context of neighborhood rough set has not been taken into consideration. In this paper, a novel rough set model, Dominance-based Neighborhood Rough Sets (DNRS), is proposed which aims to process a decision system with hybrid data types where the partial order between objects is taken into consideration. The properties of DNRS are studied. Attribute reduction under DNRS is investigated.
Read more7Attribute reduction based on misclassification cost in variable precision rough set model
As an effective tool for knowledge acquisition, attribute reduction is one of the key issues in rough set theory. In current research, most researchers choose to reduce redundant attributes as many as possible through some attribution reduction algorithms. However, the misclassification cost induced by attribute reduction is ignored. Thus, it is worth studying that how to reduce redundant attributes based on preserving the misclassification cost. Firstly, in this paper, the degree of minimum misclassification is defined. Then, by introducing decision process into variable precision rough set, a new model which is based on minimum misclassification cost with variable precision rough set (VPRS) is proposed. Moreover, based on the minimum misclassification cost, a heuristic attribute reduction algorithm is proposed. Finally, the simulation result shows that a feasible and reliable set of attributes can be obtained with our algorithm. These results further enrich attribute reduction to effectively deal with the uncertain classification problems.
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4Attribute reduction based on a rapid variable granular ball generation model
Attribute reduction is a key step in processing large-scale datasets, where the Granular Ball Neighborhood Rough Set (GBNRS) can significantly enhance the performance of attribute reduction compared to the traditional Neighborhood Rough Set (NRS). However, the GBNRS algorithm faces such challenges as a sharp increase in computational costs in high-dimensional spaces. To address these issues, this study introduces a new granular ball quality index to judge the separability degree of decision classes, and on the basis of this index, a rapid variable granular ball generation model (RVGBGM) is proposed. Compared with GBNRS, RVGBGM has the following advantages: (1) it reduces the number of granular balls and can quickly reflect the separability degree of different decision classes with few granular balls, (2) it constructs granular balls by using label information and shortens the time of granular ball construction, and (3) it can adjust the radius of granular balls adaptively by using parameters to determine the optimal granular ball radius for different datasets. Finally, we compare the RVGBGM algorithm with classical attribute reduction algorithms and the current state-of-the-art granular ball algorithm on 11 datasets. The proposed algorithm significantly improves algorithm efficiency while maintaining high accuracy. • A variable granular ball is defined. • A novel performance measure called separability degree between two variable granular balls is presented. • An efficient attribute reduction algorithm based on variable granular ball is devised and verified.
Read moreAttribute Reduction Based on Rough Neighborhood Approximation
One of the main obstacles facing current data mining techniques is attribute reduction. This paper discusses the basic concepts of rough set, and studies two rough approximations operators under neighborhood systems. An attribute reduction method based on rough set theory and neighborhood systems is presented. The experimental results show that the method of attributes reduction with rough sets and neighborhood system is feasible and valid.
Read more25Attribute reduction with personalized information granularity of nearest mutual neighbors
Attribute reduction with personalized information granularity of nearest mutual neighbors
133A novel approach to attribute reduction based on weighted neighborhood rough sets
A novel approach to attribute reduction based on weighted neighborhood rough sets
52A class-specific feature selection and classification approach using neighborhood rough set and K-nearest neighbor theories
A class-specific feature selection and classification approach using neighborhood rough set and K-nearest neighbor theories
Read moreFast Attribute Reduction for Big Datasets Based on Neighborhood Rough Set
Neighborhood rough set (NRS) is usually only applicable to small datasets due to the large number of useless and repetitive neighborhood calculations, which severely limits the efficiency of NRS. Many studies improve the efficiency of NRS by narrowing the neighborhood search range down and achieve good performance on small datasets, but they do not perform well on big datasets. To further improve the efficiency on big datasets, we propose a fast attribute reduction method for big datasets based on NRS (FARforBD). In addition, a theorem is also represented to prove the correctness and effectiveness of the proposed method. In FARforBD, we further reduce the neighborhood search range to a neighborhood without any positive region samples. This method greatly reduces many useless neighborhood calculations. The comparison experiments on big datasets show the effectiveness and efficiency of FARforBD.
Read more1Attribute Reduction and Decision Tree Pruning to Simplify Liver Fibrosis Prediction Algorithms A Cohort Study
Background: Assessment of liver fibrosis is a vital need for enabling therapeutic decisions and prognostic evaluations of chronic hepatitis. Liver biopsy is considered the definitive investigation for assessing the stage of liver fibrosis but it carries several limitations. FIB-4 and APRI also have a limited accuracy. The National Committee for Control of Viral Hepatitis (NCCVH) in Egypt has supplied a valuable pool of electronic patients’ data that data mining techniques can analyze to disclose hidden patterns, trends leading to the evolution of predictive algorithms. Aim: to collaborate with physicians to develop a novel reliable, easy to comprehend noninvasive model to predict the stage of liver fibrosis utilizing routine workup, without imposing extra costs for additional examinations especially in areas with limited resources like Egypt. Methods: This multi-centered retrospective study included baseline demographic, laboratory, and histopathological data of 69106 patients with chronic hepatitis C. We started by data collection preprocessing, cleansing and formatting for knowledge discovery of useful information from Electronic Health Records EHRs. Data mining has been used to build a decision tree (Reduced Error Pruning tree (REP tree)) with 10-fold internal cross-validation. Histopathology results were used to assess accuracy for fibrosis stages. Machine learning feature selection and reduction (CfsSubseteval / best first) reduced the initial number of input features (N=15) to the most relevant ones (N=6) for developing the prediction model. Results: In this study, 32419 patients had F(0-1), 25073 had F(2) and 11615 had F(3-4). FIB-4 and APRI revalidation in our study showed low accuracy and high discordance with biopsy results, with overall AUC 0.68 and 0.58 respectively. Out of 15 attributes machine learning selected Age, AFP, AST, glucose, albumin, and platelet as the most relevant attributes. Results for REP tree indicated an overall classification accuracy up to 70% and ROC Area 0.74 which was not nearly affected by attribute reduction, and pruning . However attribute reduction, and tree pruning were associated with simpler model easy to understand by physician with less time for execution. Conclusion: This study we had the chance to study a large cohort of 69106 chronic hepatitis patients with available liver biopsy results to revise and validate the accuracy of FIB-4 and APRI. This study represents the collaboration between computer scientist and hepatologists to provide clinicians with an accurate novel and reliable, noninvasive model to predict the stage of liver fibrosis.
Read moreA New Method of Attribute Reduction and Prediction in Fuzzy Decision System
The theory of rough sets has become well established as an approach for uncertainty management in a wide variety of applications. Various fuzzy generalizations of rough approximations have been made over the years. A new framework for the study of low approximation, upper approximation, attribute reduction are defined in fuzzy decision system in which both lower bound reduction, upper bound reduction, bound reduction are given. Rule extraction and prediction are investigated through one example.
Read more8Fuzzy object-induced network three-way concept lattice and its attribute reduction
Fuzzy object-induced network three-way concept lattice and its attribute reduction
55Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction
Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction
125Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions
Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions
1Attribute reduction based on combinatorial cross-entropy algorithm
To acquire the optimal attribute reduction, a novel method was proposed on the basis of cross-entropy algorithm. The method divides the samples generated by the cross entropy into the elite samples and the common samples, and generates a good sample set for the variation of the elite samples and the update of the common samples. The model of attribute reduction of rough set is solved by using the improved combined cross entropy algorithm. Applied to the UCI data concentration tested and compared with the other algorithms, the improved combined cross entropy algorithm of attribute reduction is proved to be feasible. What's more, the algorithm can achieve the smallest reduction of data sets and has a high precision and classification accuracy.
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