scholarly journals Neighborhood Attribute Reduction: A Multicriterion Strategy Based on Sample Selection

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 282 ◽  
Author(s):  
Yuan Gao ◽  
Xiangjian Chen ◽  
Xibei Yang ◽  
Pingxin Wang
Keyword(s):  
Rough Set ◽  
Heuristic Algorithm ◽  
Sample Selection ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Multiple Criteria ◽  
Experimental Results ◽  
Time Consumption ◽  
Redundant Data ◽  
Selection For

In the rough-set field, the objective of attribute reduction is to regulate the variations of measures by reducing redundant data attributes. However, most of the previous concepts of attribute reductions were designed by one and only one measure, which indicates that the obtained reduct may fail to meet the constraints given by other measures. In addition, the widely used heuristic algorithm for computing a reduct requires to scan all samples in data, and then time consumption may be too high to be accepted if the size of the data is too large. To alleviate these problems, a framework of attribute reduction based on multiple criteria with sample selection is proposed in this paper. Firstly, cluster centroids are derived from data, and then samples that are far away from the cluster centroids can be selected. This step completes the process of sample selection for reducing data size. Secondly, multiple criteria-based attribute reduction was designed, and the heuristic algorithm was used over the selected samples for computing reduct in terms of multiple criteria. Finally, the experimental results over 12 UCI datasets show that the reducts obtained by our framework not only satisfy the constraints given by multiple criteria, but also provide better classification performance and less time consumption.

scholarly journals Pseudolabel Decision-Theoretic Rough Set

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Xun Wang ◽  
Wendong Zhang ◽  
Dun Liu ◽  
Hualong Yu ◽  
Xibei Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Rough Set ◽  
Heuristic Algorithm ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Experimental Results ◽  
Data Sets ◽  
Binary Relations ◽  
Sensitivity Problem ◽  
Neighborhood Relation

In decision-theoretic rough set (DTRS), the decision costs are used to generate the thresholds for characterizing the probabilistic approximations. Similar to other rough sets, many generalized DTRS can also be formed by using different binary relations. Nevertheless, it should be noticed that most of the processes for calculating binary relations do not take the labels of samples into account, which may lead to the lower discrimination; for example, samples with different labels are regarded as indistinguishable. To fill such gap, the main contribution of this paper is to propose a pseudolabel strategy for constructing new DTRS. Firstly, a pseudolabel neighborhood relation is presented, which can differentiate samples by not only the neighborhood technique but also the pseudolabels of samples. Immediately, the pseudolabel neighborhood decision-theoretic rough set (PLNDTRS) can be constructed. Secondly, the problem of attribute reduction is explored, which aims to further reduce the PLNDTRS related decision costs. A heuristic algorithm is also designed to find such reduct. Finally, the clustering technique is employed to generate the pseudolabels of samples; the experimental results over 15 UCI data sets tell us that PLNDTRS is superior to DTRS without using pseudolabels because the former can generate lower decision costs. Moreover, the proposed heuristic algorithm is also effective in providing satisfied reducts. This study suggests new trends concerning cost sensitivity problem in rough data analysis.

scholarly journals Ensemble and Quick Strategy for Searching Reduct: A Hybrid Mechanism

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Wangwang Yan ◽  
Yan Chen ◽  
Jinlong Shi ◽  
Hualong Yu ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
High Stability ◽  
State Of The Art ◽  
Attribute Reduction ◽  
Experimental Results ◽  
Data Sets ◽  
Time Consumption ◽  
Fitness Functions ◽  
Hybrid Mechanism

Attribute reduction is commonly referred to as the key topic in researching rough set. Concerning the strategies for searching reduct, though various heuristics based forward greedy searchings have been developed, most of them were designed for pursuing one and only one characteristic which is closely related to the performance of reduct. Nevertheless, it is frequently expected that a justifiable searching should explicitly involves three main characteristics: (1) the process of obtaining reduct with low time consumption; (2) generate reduct with high stability; (3) acquire reduct with competent classification ability. To fill such gap, a hybrid based searching mechanism is designed, which takes the above characteristics into account. Such a mechanism not only adopts multiple fitness functions to evaluate the candidate attributes, but also queries the distance between attributes for determining whether two or more attributes can be added into the reduct simultaneously. The former may be useful in deriving reduct with higher stability and competent classification ability, and the latter may contribute to the lower time consumption of deriving reduct. By comparing with 5 state-of-the-art algorithms for searching reduct, the experimental results over 20 UCI data sets demonstrate the effectiveness of our new mechanism. This study suggests a new trend of attribute reduction for achieving a balance among various characteristics.

A Heuristic Attribute Reduction Based on Multi-Granularity Rough Set

2014 ◽  
Vol 513-517 ◽  
pp. 973-977
Author(s):  
Zhi Li Pei ◽  
Jian Hong Qi ◽  
Li Sha Liu ◽  
Qing Hu Wang ◽  
Ming Yang Jiang ◽  
...  
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Attribute Reduction ◽  
Experimental Results ◽  
Attribute Importance

In 2012, Wang Zuofei built up granularity-function and applied it to the measure of attribute importance and attribute reduction. On this basis, granularity-function based upon pessimistic and optimistic multi-granularity rough set is constructed. It is applied to the calculation of attribute importance and attribute reduction. According to the experimental results, the method can reduce the dimension of features and obviously improve the classification accuracy and efficiency.

A Weighted Naive Bayes Algorithm Based on the Attribute Order Reduction

2013 ◽  
Vol 718-720 ◽  
pp. 2108-2112 ◽  
Author(s):  
Xi Zhou ◽  
Ke Luo
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Attribute Reduction ◽  
Order Reduction ◽  
Classification Performance ◽  
Classification Method ◽  
Bayes Classifier ◽  
Attribute Order ◽  
Bayes Algorithm

Naïve Bayes classifier was generally considered as a simple and efficient classification method. However, its classification performance was affected to some extent because of the assuming that the conditions properties were independent of each other. By analyzing the classification principle and improvement of Bayesian and the Attribute Reduction of Rough Set, this paper proposed a Naïve Bayes algorithm that the attribute order reduction and weighting were improved simultaneously. Experiment results demonstrated that the proposed method performed well in classification accuracy.

Sample Pair Selection for Attribute Reduction with Rough Set

2012 ◽  
Vol 24 (11) ◽  
pp. 2080-2093 ◽  
Author(s):  
Degang Chen ◽  
Suyun Zhao ◽  
Lei Zhang ◽  
Yongping Yang ◽  
Xiao Zhang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Selection For ◽  
Sample Pair

Combined Accelerator for Attribute Reduction: A Sample Perspective

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Chen ◽  
Jingjing Song ◽  
Keyu Liu ◽  
Yaojin Lin ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Data Sets ◽  
Multiple Views ◽  
Time Consumption ◽  
Elapsed Time ◽  
Rough Approximation ◽  
Neighborhood Rough Set ◽  
Speed Up ◽  
Neighborhood Relation

In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

scholarly journals An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 155 ◽  
Author(s):  
Lin Sun ◽  
Xiaoyu Zhang ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Data Sets ◽  
Complex Data ◽  
Decision Systems ◽  
Neighborhood Rough Sets

Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.

Neighbor Inconsistent Pair Selection for Attribute Reduction by Rough Set Approach

2018 ◽  
Vol 26 (2) ◽  
pp. 937-950 ◽  
Author(s):  
Jianhua Dai ◽  
Qinghua Hu ◽  
Hu Hu ◽  
Debiao Huang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Selection For
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