K-size partial reduct: Positive region optimization for attribute reduction

2021 ◽  
pp. 107253
Author(s):  
Xiaojun Xie ◽  
Xingjian Gu ◽  
Yanbin Li ◽  
Zhiwei Ji
Keyword(s):  
Attribute Reduction ◽  
Positive Region

PARA: A positive-region based attribute reduction accelerator

2019 ◽  
Vol 503 ◽  
pp. 533-550 ◽  
Author(s):  
Peng Ni ◽  
Suyun Zhao ◽  
Xizhao Wang ◽  
Hong Chen ◽  
Cuiping Li
Keyword(s):  
Attribute Reduction ◽  
Positive Region

Feature Reduction with Inconsistency

2010 ◽  
Vol 4 (2) ◽  
pp. 77-87 ◽  
Author(s):  
Yong Liu ◽  
Yunliang Jiang ◽  
Jianhua Yang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Rough Set ◽  
Attribute Reduction ◽  
Classical Problem ◽  
Feature Reduction ◽  
Temporal Complexity ◽  
Semantic Correlation ◽  
Positive Region ◽  
Original Feature

Feature selection is a classical problem in machine learning, and how to design a method to select the features that can contain all the internal semantic correlation of the original feature set is a challenge. The authors present a general approach to select features via rough set based reduction, which can keep the selected features with the same semantic correlation as the original feature set. A new concept named inconsistency is proposed, which can be used to calculate the positive region easily and quickly with only linear temporal complexity. Some properties of inconsistency are also given, such as the monotonicity of inconsistency and so forth. The authors also propose three inconsistency based attribute reduction generation algorithms with different search policies. Finally, a “mini-saturation” bias is presented to choose the proper reduction for further predictive designing.

scholarly journals A data fusion method in wireless sensor network based on belief structure

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chengfeng Long ◽  
Xingxin Liu ◽  
Yakun Yang ◽  
Tao Zhang ◽  
Siqiao Tan ◽  
...  
Keyword(s):  
Data Fusion ◽  
Attribute Reduction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Method ◽  
Processing Efficiency ◽  
Belief Structure ◽  
High Data ◽  
Positive Region ◽  
Different Levels

AbstractConsidering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

scholarly journals A Fast Attribute Reduction Algorithm Based on a Positive Region Sort Ascending Decision Table

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1189 ◽  
Author(s):  
Linzi Yin ◽  
Zhaohui Jiang
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Experimental Tests ◽  
Decision Table ◽  
The Novel ◽  
Reduction Algorithm ◽  
Positive Region ◽  
Efficient Reduction ◽  
Key Steps ◽  
High Computational Efficiency

Attribute reduction is one of the challenging problems in rough set theory. To accomplish an efficient reduction algorithm, this paper analyzes the shortcomings of the traditional methods based on attribute significance, and suggests a novel reduction way where the traditional attribute significance calculation is replaced by a special core attribute calculation. A decision table called the positive region sort ascending decision table (PR-SADT) is defined to optimize some key steps of the novel reduction method, including the special core attribute calculation, positive region calculation, etc. On this basis, a fast reduction algorithm is presented to obtain a complete positive region reduct. Experimental tests demonstrate that the novel reduction algorithm achieves obviously high computational efficiency.

Feature Reduction with Inconsistency

2012 ◽  
pp. 195-204
Author(s):  
Yong Liu ◽  
Yunliang Jiang ◽  
Jianhua Yang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Rough Set ◽  
Attribute Reduction ◽  
Classical Problem ◽  
Feature Reduction ◽  
Temporal Complexity ◽  
Semantic Correlation ◽  
Positive Region ◽  
Original Feature

Feature selection is a classical problem in machine learning, and how to design a method to select the features that can contain all the internal semantic correlation of the original feature set is a challenge. The authors present a general approach to select features via rough set based reduction, which can keep the selected features with the same semantic correlation as the original feature set. A new concept named inconsistency is proposed, which can be used to calculate the positive region easily and quickly with only linear temporal complexity. Some properties of inconsistency are also given, such as the monotonicity of inconsistency and so forth. The authors also propose three inconsistency based attribute reduction generation algorithms with different search policies. Finally, a “mini-saturation” bias is presented to choose the proper reduction for further predictive designing.

Attribute reduction for multi-label classification based on labels of positive region

2020 ◽  
Vol 24 (18) ◽  
pp. 14039-14049 ◽  
Author(s):  
Xiaodong Fan ◽  
Qi Chen ◽  
Zhijun Qiao ◽  
Changzhong Wang ◽  
Mingyan Ten
Keyword(s):  
Attribute Reduction ◽  
Positive Region

scholarly journals A Data Fusion Method in Wireless Sensor Network Based on Belief Structure

2020 ◽  
Author(s):  
Chenfeng Long ◽  
Xinxing Liu ◽  
Yakun Yang ◽  
Tao Zhang ◽  
Siqiao Tan ◽  
...  
Keyword(s):  
Data Fusion ◽  
Attribute Reduction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fusion Method ◽  
Processing Efficiency ◽  
Belief Structure ◽  
High Data ◽  
Positive Region ◽  
Different Levels

Abstract Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation order information system, and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction is unnecessary and sufficient conditional plausibility reduction in the same level; if the cover structure order of different levels are the same, the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

Positive Region Reduct Based on Relative Discernibility and Acceleration Strategy

2018 ◽  
Vol 26 (04) ◽  
pp. 521-551 ◽  
Author(s):  
Hao Ge ◽  
Chuanjian Yang ◽  
Longshu Li
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Reduction Process ◽  
High Volume ◽  
Decision Table ◽  
High Dimensional ◽  
Classical Type ◽  
Data Sets ◽  
Radix Sort ◽  
Positive Region

Attribute reduction is one of key issues in rough set theory, and positive region reduct is a classical type of reducts. However, a lot of reduction algorithms have more high time expenses when dealing with high-volume and high-dimensional data sets. To overcome this shortcoming, in this paper, a relative discernibility reduction method based on the simplified decision table of the original decision table is researched for obtaining positive region reduct. Moreover, to further improve performance of reduction algorithm, we develop an accelerator for attribute reduction, which reduces the radix sort times of the reduction process to raise algorithm efficiency. By the accelerator, two positive region reduction algorithms, i.e., FARA-RS and BARA-RS, based on the relative discernibility are designed. FARA-RS simultaneously reduce the size of the universe and the number of radix sort to achieve speedup and BARA-RS only reduce the number of radix sort to achieve acceleration. The experimental results show that the proposed reduction algorithms are effective and feasible for high dimensional and large data sets.

Application of an attribute reduction algorithm based on discernibility matrix in PBNM

2014 ◽  
Author(s):  
Shuo Feng ◽  
Haiying Chu ◽  
Xuyang Wang ◽  
Yuanka Liang ◽  
Xianwei Shi ◽  
...  
Keyword(s):  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Discernibility Matrix
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