Results 261 to 270 of about 5,614 (289)

Palmprint Recognition Based on Neighborhood Rough Set

2010
Feature extraction is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Neighborhood rough set (NRS) based feature extracting algorithm is able to delete most of the redundant and irrelevant features, which avoid the step of data discretization and hence decreased the information lost in preprocess. In
Shanwen Zhang, Jiandu Liu
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Uncertainty measures of Neighborhood System-based rough sets

Knowledge-Based Systems, 2015
We first study the uncertainty of rough sets in binary GrC Model (Third GrC Model).We first propose the rough memberships to depict rough sets.Construct fuzzy entropy based on rough memberships and prove its rationality.We first propose the rough intuitionistic memberships to depict rough sets.Construct intuitionistic fuzzy entropy to measure ...
Tingting Zheng, Linyun Zhu
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Generalized rough set model based on the intersection of neighborhoods

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015
Various generalized rough set models based on successor (predecessor) neighborhoods were discussed in the literature. In this paper, by using the intersection of all successor (predecessor) neighborhoods which contain the same object as knowledge granule, the subset lower and upper approximations and concept lower and upper approximations are defined ...
Jianting Shen, Yanyong Guan, Hongkai Wang 0001, Jilin Huang   +3 more
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Neighborhood Rough Sets based Multi-Label classification

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
Nowadays, multi-label classification methods are of growing interest. Due to the relationships among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents a novel multi-label classification framework based on the variable precision neighborhood rough sets ...
Ying Yu, Witold Pedrycz, Duoqian Miao 0001, Hongyun Zhang   +3 more
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Knowledge Granulation Based Roughness Measure for Neighborhood Rough Sets

2013 Third International Conference on Intelligent System Design and Engineering Applications, 2013
Neighborhood rough sets have been applied to feature selection and attribute reduction successfully. Roughness is an important uncertainty measure for a concept in an information system. In this paper, generalized from the classical roughness, a new uncertainty measure based on granulation of knowledge for neighborhood rough sets is proposed to ...
Chengdong Yang, Jianlong Qiu, Wenyin Zhang   +2 more
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Neighborhood rough set with neighborhood equivalence relation for feature selection

Knowledge and Information Systems, 2023
Shangzhi Wu, Litai Wang, Shuyue Ge, Zhengwei Hao, Yulin Liu   +4 more
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NEIGHBORHOOD SYSTEM BASED ROUGH SET: MODELS AND ATTRIBUTE REDUCTIONS

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2012
The neighborhood system based rough set is a generalization of Pawlak's rough set model since the former uses the neighborhood system instead of the partition for constructing target approximation. In this paper, the neighborhood system based rough set approach is employed to deal with the incomplete information system. By the coverings induced by the
Xibei Yang, Zehua Chen, Huili Dou, Ming Zhang 0033, Jingyu Yang 0001   +4 more
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Exploring Neighborhood Structures with Neighborhood Rough Sets in Classification Learning

2013
We introduce neighborhoods of samples to granulate the universe and use the neighborhood granules to approximate classification, thus they derived a model of neighborhood rough sets. Some machine learning algorithms, including boundary sample selection, feature selection and rule extraction, were developed based on the model.
Qinghua Hu, Leijun Li, Pengfei Zhu
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Label distribution feature selection based on neighborhood rough set

Concurrency and Computation: Practice and Experience
SummaryIn label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature.
Yilin Wu 0001, Wenzhong Guo, Yaojin Lin
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GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set

IEEE Transactions on Neural Networks and Learning Systems
Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent ...
Shuyin Xia, Cheng Wang 0050, Guoyin Wang 0001, Xinbo Gao 0001, Weiping Ding 0001, JianHang Yu, Yujia Zhai, Zizhong Chen   +7 more
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