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Axiomatization on generalized neighborhood system-based rough sets
Soft Computing, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fangfang Zhao, Lingqiang Li
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A study of rough sets based on 1-neighborhood systems
Information Sciences, 2013Abstract A special class of neighborhood systems, called 1-neighborhood systems, are important in rough set theory. By using a concept “core” originated in general topology, we define two types of rough sets based on 1-neighborhood systems in this paper.
Zuoming Yu, Xiaole Bai, Ziqiu Yun
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Neighborhood rough set reduction with fish swarm algorithm
Soft Computing, 2016Feature reduction refers to the problem of deleting those input features that are less predictive of a given outcome; a problem encountered in many areas such as pattern recognition, machine learning and data mining. In particular, it has been successfully applied in tasks that involve datasets containing huge numbers of features.
Yumin Chen 0002 +2 more
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Palmprint Recognition Based on Neighborhood Rough Set
2010Feature 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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Generalized rough set model based on the intersection of neighborhoods
2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015Various 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 +3 more
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Neighborhood Rough Sets based Multi-Label classification
2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013Nowadays, 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 +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, 2013Neighborhood 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 +2 more
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Neighborhood rough set with neighborhood equivalence relation for feature selection
Knowledge and Information Systems, 2023Shangzhi Wu +4 more
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NEIGHBORHOOD SYSTEM BASED ROUGH SET: MODELS AND ATTRIBUTE REDUCTIONS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2012The 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 +4 more
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Exploring Neighborhood Structures with Neighborhood Rough Sets in Classification Learning
2013We 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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