Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions

2017 ◽  
Vol 415-416 ◽  
pp. 213-232 ◽  
Author(s):  
Chen Hao ◽  
Jinhai Li ◽  
Min Fan ◽  
Wenqi Liu ◽  
Eric C.C. Tsang
Keyword(s):  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Decision Tables

scholarly journals Optimal scale combination selection for inconsistent multi-scale decision tables

2021 ◽  
Author(s):  
Yingjie Zhu ◽  
Bin Yang
Keyword(s):  
Lattice Model ◽  
Selection Model ◽  
Decision Table ◽  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Model Based ◽  
Positive Region ◽  
Decision Tables ◽  
Combination Selection

Abstract Hierarchical structured data are very common for data mining and other tasks in real-life world. How to select the optimal scale combination from a multi-scale decision table is critical for subsequent tasks. At present, the models for calculating the optimal scale combination mainly include lattice model, complement model and stepwise optimal scale selection model, which are mainly based on consistent multi-scale decision tables. The optimal scale selection model for inconsistent multi-scale decision tables has not been given. Based on this, firstly, this paper introduces the concept of complement and lattice model proposed by Li and Hu. Secondly, based on the concept of positive region consistency of inconsistent multi-scale decision tables, the paper proposes complement model and lattice model based on positive region consistent and gives the algorithm. Finally, some numerical experiments are employed to verify that the model has the same properties in processing inconsistent multi-scale decision tables as the complement model and lattice model in processing consistent multi-scale decision tables. And for the consistent multi-scale decision table, the same results can be obtained by using the model based on positive region consistent. However, the lattice model based on positive region consistent is more time-consuming and costly. The model proposed in this paper provides a new theoretical method for the optimal scale combination selection of the inconsistent multi-scale decision table.

Optimal scale selection and attribute reduction in multi-scale decision tables based on three-way decision

2020 ◽  
Vol 541 ◽  
pp. 36-59 ◽  
Author(s):  
Yunlong Cheng ◽  
Qinghua Zhang ◽  
Guoyin Wang ◽  
Bao Qing Hu
Keyword(s):  
Attribute Reduction ◽  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Decision Tables

Double-quantitative rough sets, optimal scale selection and reduction in multi-scale dominance IF decision tables

2021 ◽  
Vol 130 ◽  
pp. 170-191
Author(s):  
Bing Huang ◽  
Huaxiong Li ◽  
Guofu Feng ◽  
Chunxiang Guo ◽  
Dafeng Chen
Keyword(s):  
Rough Sets ◽  
Scale Selection ◽  
Multi Scale ◽  
Optimal Scale ◽  
Decision Tables

scholarly journals Matrix Method for the Optimal Scale Selection of Multi-Scale Information Decision Systems

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 290 ◽  
Author(s):  
Ying Chen ◽  
Jin Li ◽  
Jian Huang
Keyword(s):  
Information Systems ◽  
Matrix Method ◽  
Scale Selection ◽  
Decision Systems ◽  
Multi Scale ◽  
Optimal Scale ◽  
The Matrix ◽  
Properties Of Information ◽  
Selection Algorithms ◽  
Multi Scales

In multi-scale information systems, the information is often characterized at multi scales and multi levels. To facilitate the computational process of multi-scale information systems, we employ the matrix method to represent the multi-scale information systems and to select the optimal scale combination of multi-scale decision information systems in this study. To this end, we first describe some important concepts and properties of information systems using some relational matrices. The relational matrix is then introduced into multi-scale information systems, and used to describe some main concepts in systems, including the lower and upper approximate sets and the consistence of systems. Furthermore, from the view of the relation matrix, the scale significance is defined to describe the global optimal scale and the local optimal scale of multi-scale information systems. Finally, the relational matrix is used to compute the scale significance and to construct the optimal scale selection algorithms. The efficiency of these algorithms is examined by several practical examples and experiments.

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