Attribute selection approaches for incomplete interval-value data

2021 ◽  
Vol 40 (5) ◽  
pp. 8775-8792
Author(s):  
Zhaowen Li ◽  
Shimin Liao ◽  
Liangdong Qu ◽  
Yan Song
Keyword(s):  
Information System ◽  
Information Entropy ◽  
Attribute Selection ◽  
Discernibility Matrix ◽  
Similarity Degree ◽  
Tolerance Relation ◽  
Interval Valued ◽  
Information Values ◽  
Interval Value

Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, θ-reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction algorithms base on θ-discernibility matrix, θ-information entropy and θ-significance in an IIVIS are given.

scholarly journals Multiple Granulation Rough Set Approach to Interval-Valued Intuitionistic Fuzzy Ordered Information Systems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 949
Author(s):  
Zhen Li ◽  
Xiaoyan Zhang
Keyword(s):  
Information Theory ◽  
Pattern Recognition ◽  
Information System ◽  
Information Systems ◽  
Rough Set ◽  
Fuzzy Set ◽  
Equivalence Relation ◽  
Target Concept ◽  
Actual Case ◽  
Interval Valued

As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which do not apply to the IIFS. In this paper, we combine the IIFS with the ordered information system to obtain the interval-valued intuitive fuzzy ordered information system (IIFOIS). On this basis, three types of multiple granulation rough set models based on the dominance relation are established to effectively overcome the limitation mentioned above, which belongs to the interdisciplinary subject of information theory in mathematics and pattern recognition. First, for an IIFOIS, we put forward a multiple granulation rough set (MGRS) model from two completely symmetry positions, which are optimistic and pessimistic, respectively. Furthermore, we discuss the approximation representation and a few essential characteristics for the target concept, besides several significant rough measures about two kinds of MGRS symmetry models are discussed. Furthermore, a more general MGRS model named the generalized MGRS (GMGRS) model is proposed in an IIFOIS, and some important properties and rough measures are also investigated. Finally, the relationships and differences between the single granulation rough set and the three types of MGRS are discussed carefully by comparing the rough measures between them in an IIFOIS. In order to better utilize the theory to realistic problems, an actual case shows the methods of MGRS models in an IIFOIS is given in this paper.

New measures of uncertainty for an interval-valued information system

2019 ◽  
Vol 470 ◽  
pp. 156-174 ◽  
Author(s):  
Ningxin Xie ◽  
Meng Liu ◽  
Zhaowen Li ◽  
Gangqiang Zhang
Keyword(s):  
Information System ◽  
Interval Valued

scholarly journals Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras

2020 ◽  
pp. 83-88
Author(s):  
Nurhidayat ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Information System ◽  
Rough Set ◽  
Access Point ◽  
Learning Processes ◽  
Discernibility Matrix ◽  
Student Work ◽  
Hard Drives ◽  
Good Shape ◽  
Equivalency Class

Hardware is a computer that can be seen and touched in person. Hardware is used to support student work and learning processes. The hardware should always be in good shape. If any damage should be done quickly. The benefits of this study provide a viable level of data against hardware tools. The purpose of this study determines that hardware that is worth using quickly and precisely so easily can be repaired and replaced. Hard-processed action consists of 12 projectors, 2 units of access point, 6 units of monitors, and 20 CPU units. To see the level of appropriateness regarding hard drives requires a rough set algorithm with that stage: information system; Decision system; Equivalency class; Discernibility matrix; Discernibility Matrix module D; Reduction; Generate Rules. The results of the 40 devices of study STMIK Indonesia Padang subtract college have 10 rules of policy on whether the hardware is still viable, repaired or replaced. So using a rough set algorithm is particularly appropriate to apply in a verifiable level of accuracy to fast and precise hardware.

On variable-precision-based rough set approach to incomplete interval-valued fuzzy information systems and its applications

2021 ◽  
Vol 40 (1) ◽  
pp. 463-475
Author(s):  
Juan Li ◽  
Yabin Shao ◽  
Xiaoding Qi
Keyword(s):  
Information System ◽  
Information Systems ◽  
Attribute Reduction ◽  
Misclassification Rate ◽  
Fuzzy Information ◽  
Attribute Weights ◽  
Variable Precision ◽  
Multiple Attribute ◽  
Rough Approximation ◽  
Interval Valued

 With respect to multiple attribute group decision making problems in which the attribute weights and the expert weights take the form of real numbers and the attribute values take the form of interval-valued uncertain linguistic variable. In this paper, we introduce the idea of variable precision into the incomplete interval-valued fuzzy information system and propose the theory of variable precision rough sets over incomplete interval-valued fuzzy information systems. Then, we give the properties of rough approximation operators and study the knowledge discovery and attribute reduction in the incomplete interval-valued fuzzy information system under the condition that a certain degree of misclassification rate is allowed to exist. Furthermore, a decision rule and decision model are given. Finally, an illustrative example is given and compared with the existing methods, the practicability and effectiveness of this method are further verified.

Information structures and uncertainty in an image information system

2021 ◽  
Vol 40 (1) ◽  
pp. 295-317
Author(s):  
Gangqiang Zhang ◽  
Zhaowen Li ◽  
Pengfei Zhang ◽  
Ningxin Xie
Keyword(s):  
Information System ◽  
Kernel Method ◽  
Attribute Reduction ◽  
Gaussian Kernel ◽  
Information Structures ◽  
Image Information ◽  
Effectiveness Analysis ◽  
Uncertainty Measurement ◽  
Properties Of Information ◽  
Information Values

An information system as a database that stands for relationships between objects and attributes is an important mathematical model. An image information system is an information system where each of its information values is an image and its information structures embody internal features of this type of information system. Uncertainty measurement is an effective tool for evaluation. This paper explores measures of uncertainty for an information system by using the proposed information structures. The distance between two objects in an image information system is first given. After that, the fuzzy Tcos-equivalence relation, induced by this system by using Gaussian kernel method, is obtained, where Gaussian kernel is based on this distance. Next, information structures of this system are described by set vectors, dependence between information structures is studied and properties of information structures are given by using inclusion degree, and application for information structures and uncertainty measures of an image information system are investigated by the information structures. Moreover, effectiveness analysis is done to show the feasibility of the proposed measures from the angle of statistics. Finally, an application of the proposed measurement for attribute reduction is given. These results will be helpful for understanding the essence of uncertainty in an image information system.

A New Incremental Updating Algorithm for Core Based on Simplified Discernibility Matrix

2011 ◽  
Vol 267 ◽  
pp. 931-936
Author(s):  
Chang Sheng Zhang
Keyword(s):  
Information System ◽  
Theoretical Analysis ◽  
Efficient Algorithm ◽  
Discernibility Matrix ◽  
Incremental Updating ◽  
Inconsistent Information

Firstly, the concept of simplified information system is introduced, the notion of simplified discerniblity matrix is put forward and the method for computing core based on the simplified discerniblity matrix, which can well deal with inconsistent information system. And it is proved that core based on the simplified discernibility matrix is equivalent to that based on the previous one, on the basis of that, an efficient algorithm incremental updating for core is presented, which only need to analyze the updating parts of discernibility matrix and doesn’t need to re-calculate discerniblily matrix, when a new object is added to information system. Finally, Theoretical analysis and example results show that the algorithm is efficient and feasible.

Discernibility Matrix-Based Attribute Reduction Algorithm of Decision Table

2012 ◽  
Vol 457-458 ◽  
pp. 1230-1234 ◽  
Author(s):  
Ying He ◽  
Dan He
Keyword(s):  
Attribute Reduction ◽  
Attribute Selection ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Discernibility Matrix

A discernibility matrix-based attribute reduction algorithm of decision table is introduced in this paper, which takes the importance of attributes as the heuristic message. This method solves the problem of the attribute selection when the frequencies of decision table attributes are equal. The result shows that this method can give out simple but effective method of attribute reduction.

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