Match!
Degang Chen
North China Electric Power University
129Publications
31H-index
3,625Citations
Publications 132
Newest
#1Lianjie Dong (NCEPU: North China Electric Power University)
#2Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
Attribute reduction with rough set is a popular data analysis methodology for data dimensionality reduction. For dynamic datasets, the existing research has mainly focused on incremental attribute reduction with increasing samples (rows) or attributes (columns), but there is hardly any further research on attribute reduction for dynamic datasets with simultaneously increasing samples and attributes. This paper presents a novel incremental algorithm for attribute reduction with rough set. Firstly...
Source
#1Xiaoya Che (NCEPU: North China Electric Power University)H-Index: 1
#2Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
Last.Ju-Sheng Mi (Hebei Normal University)H-Index: 17
view all 3 authors...
Abstract Each example of multi-label data is represented in an object with its feature vector (i.e. an instance) while being related to multiple labels. Learning label correlation can effectively reduce the labels needed to be predicted and optimize the classification performance. For this reason, label correlation plays a crucial role in multi-label learning and has been explored by many existing algorithms. Generally, every label has its own indispensable features, and it is reasonable to assu...
Source
#1Yanting Guo (MUST: Macau University of Science and Technology)H-Index: 1
#2Eric C. C. Tsang (MUST: Macau University of Science and Technology)H-Index: 22
Last.Binbin Sang (Southwest Jiaotong University)H-Index: 1
view all 7 authors...
Abstract Double-quantitative decision-theoretic rough sets (Dq-DTRS) provide more comprehensive description methods for rough approximations of concepts, which lay foundations for the development of attribute reduction and rule extraction of rough sets. Existing researches on concept approximations of Dq-DTRS pay more attention to the equivalence class of each object in approximating a concept, and calculate concept approximations from the whole data set in a batch. This makes the calculation of...
1 CitationsSource
#1Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
#2Lianjie Dong (NCEPU: North China Electric Power University)
Last.Ju-Sheng Mi (Hebei Normal University)H-Index: 17
view all 3 authors...
Rough set is a data evaluation methodology to take care of uncertainty in data. Attribute reduction with rough set goals to achieve a compact and informative attribute set for a given data sets, and incremental mechanism is reasonable selection for attribute reduction in dynamic data sets. This paper focuses on introducing incremental mechanism to develop effective incremental algorithm during the arrival of new attributes in terms of approach of discerning samples. The traditional definition of...
Source
#1Yanting Guo (MUST: Macau University of Science and Technology)H-Index: 1
#2Eric C. C. Tsang (MUST: Macau University of Science and Technology)H-Index: 22
Last.Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
view all 4 authors...
Abstract Local rough sets as a generalization of classical rough sets not only inherit the advantages of classical rough sets which can handle imprecise, fuzzy and uncertain data, but also break through the limitation of classical rough sets requiring large amount of labeled data. The existing researches on local rough sets mainly use the relative quantitative information between a target concept and equivalence classes of those objects contained in the target concept to approximate the target c...
2 CitationsSource
#1Bingjiao Fan (MUST: Macau University of Science and Technology)H-Index: 3
#2Eric C. C. Tsang (MUST: Macau University of Science and Technology)H-Index: 22
Last.Wen-Tao Li (HIT: Harbin Institute of Technology)H-Index: 1
view all 5 authors...
Recently, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of cognitive concept learning. We establish a concept hierarchy structure based on the existing cognitive concept learning methods. However, none of these methods could obtain the following results: get the concept, recognize objects and distinguish between two different objects. In this paper, our focus is to construct an attribute-oriented multi-level cognitive concept le...
3 CitationsSource
#1Yanyan Yang (THU: Tsinghua University)H-Index: 2
#2Shiji Song (THU: Tsinghua University)H-Index: 27
Last.Xiao ZhangH-Index: 6
view all 4 authors...
Incremental feature selection refreshes a subset of information-rich features from added-in samples without forgetting the previously learned knowledge. However, most existing algorithms for incremental feature selection have no explicit mechanisms to handle heterogeneous data with symbolic and real-valued features. Therefore, this paper presents an incremental feature selection method for heterogeneous data with the sequential arrival of samples in group. Discernible neighborhood counting that ...
Source
#1Haibo Jiang (Huda: Hubei University)H-Index: 1
#2Jianming Zhan (Huda: Hubei University)H-Index: 1
Last.Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
view all 3 authors...
At present, there is no unified method for solving multiattribute decision-making problems. In this paper, we propose two methods that benefit from some novel fuzzy rough set models. Some theoretical preliminaries pave the way. First, by means of a fuzzy logical implicator \mathcal {I}and a triangular norm \mathcal {T}, four types of coverings-based variable precision (\mathcal {I},\mathcal {T})-fuzzy rough set models are proposed. They can be used to deal with misclassification and per...
31 CitationsSource
#1Linlin Chen (NCEPU: North China Electric Power University)H-Index: 1
#2Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
Last.Hui Wang (Ulster University)H-Index: 35
view all 3 authors...
Fuzzy similarity relation is a function to measure the similarity between two samples. It is widely used to learn knowledge under the framework of fuzzy machine learning. The selection of a suitable fuzzy similarity relation is important for the learning task. It has been pointed out that fuzzy similarity relations can be brought into the framework of kernel functions in machine learning. This fact motivates us to study fuzzy similarity relation selection for fuzzy machine learning utilizing ker...
Source
#1Degang Chen (NCEPU: North China Electric Power University)H-Index: 31
#2Weihua Xu (SWU: Southwest University)H-Index: 19
Last.Jinhai Li (Kunming University of Science and Technology)H-Index: 19
view all 3 authors...
Source
12345678910