Results 271 to 280 of about 10,917,671 (339)

An accelerator for attribute reduction based on perspective of objects and attributes

Knowledge-Based Systems, 2013
Feature selection is an active area of research in pattern recognition, machine learning and artificial intelligence, which greatly improves the performance of forecasting or classification. In rough set theory, attribute reduction, as a special form of feature selection, aims to retain the discernability of the original attribute set.
Jiye Liang, Junrong Mi, Wei Wei 0018, Feng Wang 0038   +3 more
openaire   +2 more sources

A fitting model for attribute reduction with fuzzy β-covering

Fuzzy Sets Syst., 2020
As an intuitive extension of covering-based rough set models, fuzzy β covering provides an effective means to deal with uncertain information. However, classical fuzzy β covering can not fit a given data set well, which limits its popularization and ...
Zhe-Huang Huang, Jin-Jin Li
semanticscholar   +1 more source

On Covering Attribute Sets by Reducts

2007
For any fixed natural k, there exists a polynomial in time algorithm which for a given decision table Tand given kconditional attributes recognizes if there exist a decision reduct of Tcontaining these kattributes.
Mikhail Ju. Moshkov, Andrzej Skowron, Zbigniew Suraj   +2 more
openaire   +2 more sources

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

Information Sciences, 2020
Optimal scale selection and attribute reduction are two key issues related to knowledge discovery in multi-scale decision tables (MDTs). The former is mainly used to obtain optimal scale combinations by selecting a suitable scale for each attribute ...
Yun-Long Cheng, Qinghua Zhang, Guo-Yin Wang, Bao-Qing Hu   +3 more
semanticscholar   +1 more source

Attribute Reduction: An Ensemble Strategy

2017
In rough set theory, the heuristic strategy for computing reducts does not take the stability of the selected attributes into account. An unstable reduct may imply the lower adaption to data variations. To fill such a gap, an ensemble strategy is embedded in heuristic algorithm for achieving stable reducts of variable precision fuzzy rough sets.
Suping Xu, Pingxin Wang, Jinhai Li 0001, Xibei Yang, Xiangjian Chen   +4 more
openaire   +2 more sources

Improved general attribute reduction algorithms

Information Sciences, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Baizhen Li, Zhihua Wei 0001, Duoqian Miao 0001, Nan Zhang 0041, Wen Shen 0002, Chang Gong, Hongyun Zhang, Lijun Sun   +7 more
openaire   +2 more sources

Key energy-consumption feature selection of thermal power systems based on robust attribute reduction with rough sets

Information Sciences, 2020
Mechanism analysis is the main method for energy-consumption feature selection of thermal power systems. Although this method can select a few features with specific physical meanings as the input variables for energy-consumption modeling, it may ignore ...
Lian-Jie Dong, Degang Chen, Ning-Ling Wang, Zhanhui Lu   +3 more
semanticscholar   +1 more source

Test-cost-sensitive attribute reduction

Information Sciences, 2011
In many data mining and machine learning applications, there are two objectives in the task of classification; one is decreasing the test cost, the other is improving the classification accuracy. Most existing research work focuses on the latter, with attribute reduction serving as an optional pre-processing stage to remove redundant attributes.
Fan Min 0001, Huaping He, Yuhua Qian, William Zhu 0001   +3 more
openaire   +1 more source

Local Search for Attribute Reduction

2019
Two new attribute reduction algorithms based on iterated local search and rough sets are proposed. Both algorithms start with a greedy construction of a relative reduct. Then attempts to remove some attributes to make the reduct smaller. Process of attributes selection is the main difference between the algorithms. It is random for the first one, and a
Xiaojun Xie, Ryszard Janicki, Xiaolin Qin, Wei Zhao 0061, Guangmei Huang   +4 more
openaire   +1 more source

Attribute reduction for imprecise decision tables

2012 IEEE International Conference on Granular Computing, 2012
In this paper, we investigate approaches to attribute reduction for decision tables with imprecise decision attribute values. First, we introduce imprecise decision tables, and presumable and possible decision attribute value sets. We define several meaningful object sets based on the twofold decision attribute value sets.
Masahiro Inuiguchi, Bingjun Li
openaire   +1 more source