Inconsistent Neighborhoods and Relevant Properties in Neighborhood Rough Set Models

A new neighborhood variable precision rough set modal is presented in this paper. The modal possesses the characteristics of neighborhood rough set and variable precision rough set, so it can overcome shortcomings of classic rough set which only be fit for discrete variables and sensitive to noise. Based on giving the definitions of approximate reduction, lower and upper approximate reduction, lower and upper distribution reduction, two kinds of algorithms to confirm lower and upper distribution reduction were advanced. The modal was applied to diagnose one frequency modulated water pump vibration faults. The result shows the modal is more suitable to engineering problems, because it can not only deal with continues variables but also be robust to noise.

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A neighborhood rough set model for attribute reduction without distance metric

10.1117/12.2624145 ◽

2021 ◽

Author(s):

Xingxin Chen ◽

Shuyin Xia ◽

Feng Hu

Keyword(s):

Rough Set ◽

Attribute Reduction ◽

Distance Metric ◽

Neighborhood Rough Set

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Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model

International Journal of Computational Intelligence Systems ◽

10.2991/ijcis.d.200915.004 ◽

2020 ◽

Author(s):

Yan Gao ◽

Changwei Lv ◽

Zhengjiang Wu

Keyword(s):

Rough Set ◽

Attribute Reduction ◽

Boolean Matrix ◽

Neighborhood Rough Set

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An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set

Complexity ◽

10.1155/2021/8358921 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Jing Zhang ◽

Guang Lu ◽

Jiaquan Li ◽

Chuanwen Li

Keyword(s):

Feature Selection ◽

Rough Set ◽

Small Sample Size ◽

High Dimensional Data ◽

Classification Performance ◽

Small Sample ◽

Ensemble Classification ◽

High Dimensional ◽

Sample Classification ◽

Neighborhood Rough Set

Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

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Preference Mining Using Neighborhood Rough Set Model on Two Universes

Computational Intelligence and Neuroscience ◽

10.1155/2016/6975458 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Kai Zeng

Keyword(s):

Rough Set ◽

User Satisfaction ◽

Traditional Method ◽

Cold Start ◽

Effective Solution ◽

Neighborhood Rough Set ◽

Recommendation Accuracy ◽

Item Data ◽

Two Universes ◽

Cold Start Problem

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

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Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2019100105 ◽

2019 ◽

Vol 8 (4) ◽

pp. 84-100

Author(s):

Akarsh Goyal ◽

Rahul Chowdhury

Keyword(s):

Rough Set ◽

Categorical Data ◽

Rough Set Theory ◽

Clustering Algorithms ◽

Heterogeneous Data ◽

Main Function ◽

Process Uncertainty ◽

Neighborhood Rough Set ◽

Hybrid Data ◽

Stability Issues

In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also, some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and therefore have stability issues. Thus, handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 an MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data but cannot be used robustly for hybrid data. In this article, the authors generalize the MMeR algorithm with neighborhood relations and make it a neighborhood rough set model which this article calls MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data. Also, the authors have extended the MMeNR method to make it suitable for various applications like geospatial data analysis and epidemiology.

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Tool Wear Assessment Approach Based on the Neighborhood Rough Set Model and Nearest Neighbor Model

Shock and Vibration ◽

10.1155/2020/8876187 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Ren Sheng ◽

Xiaoran Zhu

Keyword(s):

Rough Set ◽

Nearest Neighbor ◽

Milling Process ◽

Two Dimension ◽

Assessment Approach ◽

Intersection Area ◽

Neighborhood Rough Set ◽

The Difference ◽

Boundary Curves ◽

Baseline State

In order to assess the degree of wear of tool for milling process quantitatively, a new assessment approach is proposed. Firstly, making full use of the neighbor information, two sensitive features are selected by using the neighborhood rough set model, and then, boundary curves are established by using the nearest neighbor model with noncounter data in two dimension spaces. Secondly, the intersection area or expanding area is used to describe the difference between two boundary models because the intersection area or expanding area can consider the effect of distance and angle simultaneously in two dimension spaces. Thirdly, after determining a baseline state, a new quantitative assessment indicator (QAI) can be calculated based on the intersection area or expanding area. The QAI can directly measure the difference between the model of baseline state and the model of unknown state and indirectly measure the degree of wear of tool. Finally, the effectiveness of the assessment approach is proven by using the Milling Dataset which was provided by the NASA Ames Research Center.

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