His primary areas of study are Rough set, Artificial intelligence, Data mining, Pattern recognition and Algorithm. His Rough set research includes themes of Discrete mathematics, Structure and Reduction. His Artificial intelligence study frequently involves adjacent topics like Machine learning.
His Data mining research incorporates elements of Fuzzy set operations, Type-2 fuzzy sets and systems, Membership function, Defuzzification and Fuzzy logic. As part of the same scientific family, Duoqian Miao usually focuses on Pattern recognition, concentrating on Automatic image annotation and intersecting with Semantic gap, Statistical classification and Class. His studies in Algorithm integrate themes in fields like Granular computing, Complete graph and Spanning tree.
Artificial intelligence, Rough set, Pattern recognition, Data mining and Algorithm are his primary areas of study. His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence. Rough set is often connected to Reduction in his work.
His work on Discriminative model, Feature selection, Linear discriminant analysis and Dimensionality reduction as part of general Pattern recognition study is frequently connected to Metric, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Data mining research incorporates themes from Representation, Cluster analysis, Feature vector, Type-2 fuzzy sets and systems and Fuzzy logic. In general Algorithm, his work in Computation is often linked to Information system linking many areas of study.
Duoqian Miao mostly deals with Artificial intelligence, Rough set, Three way, Pattern recognition and Machine learning. He combines subjects such as Relation and Algorithm, Reduction with his study of Rough set. His biological study spans a wide range of topics, including Incremental learning and Multi-label classification.
His work on Feature extraction and Discriminative model as part of general Pattern recognition research is often related to Metric, thus linking different fields of science. His Machine learning research is multidisciplinary, incorporating perspectives in Exploit and Data set. He interconnects Multi label learning and Cluster analysis in the investigation of issues within Data mining.
His scientific interests lie mostly in Artificial intelligence, Three way, Pattern recognition, Benchmark and Fuzzy logic. His research on Artificial intelligence often connects related areas such as Machine learning. His biological study spans a wide range of topics, including Object, Salient and Image retrieval.
The concepts of his Fuzzy logic study are interwoven with issues in Algorithm and Uncertain data, Data mining, Data classification. Duoqian Miao interconnects Relation, Rough set and Cluster analysis in the investigation of issues within Fuzzy set. Duoqian Miao performs multidisciplinary study in the fields of Rough set and Conflict analysis via his papers.
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From soft sets to information systems
Daowu Pei;Duoqian Miao.
granular computing (2005)
A rough set approach to feature selection based on ant colony optimization
Yumin Chen;Duoqian Miao;Ruizhi Wang.
Pattern Recognition Letters (2010)
Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection
Deng Wang;Duoqian Miao;Chen Xie.
Expert Systems With Applications (2011)
Analysis on attribute reduction strategies of rough set
Jue Wang;Duoqian Miao.
Journal of Computer Science and Technology (1998)
Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
D. Q. Miao;Y. Zhao;Y. Y. Yao;H. X. Li.
Information Sciences (2009)
A graph-theoretical clustering method based on two rounds of minimum spanning trees
Caiming Zhong;Duoqian Miao;Ruizhi Wang.
Pattern Recognition (2010)
Rough Cluster Quality Index Based on Decision Theory
P. Lingras;Min Chen;Duoqian Miao.
IEEE Transactions on Knowledge and Data Engineering (2009)
Constructive methods of rough approximation operators and multigranulation rough sets
Xiaohong Zhang;Duoqian Miao;Caihui Liu;Meilong Le.
Knowledge Based Systems (2016)
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
J. Qian;D. Q. Miao;Z. H. Zhang;W. Li.
International Journal of Approximate Reasoning (2011)
A rough set approach to feature selection based on power set tree
Yumin Chen;Duoqian Miao;Ruizhi Wang;Keshou Wu.
Knowledge Based Systems (2011)
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