Cognitive concept learning from incomplete information

Zhao, Yingxiu; Li, Jinhai; Liu, Wenqi; Xu, Weihua

doi:10.1007/s13042-016-0553-8

Cited by 43 publications

(6 citation statements)

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“…Here, it is worth stressing that the notion of concept-cognitive learning. we know that CCL was studied from a cognitive perspective by many scholars [8,12,13,11,17,20,41,42], and to meet of different requirements, various of names were used such as cognitive model [41], cognitive system [34] and cognitive concept learning [12,42]; however, in order to stress the importance of the concept in machine learning and cognitive process, its name was also discussed by Mi and Li in 2016; and then we first used the concept of concept-cognitive learning in 2016 [17] and it is called classical CCL; moreover, an atomic concept and a composite concept were also defined in [17]. Based on it, inspired by the similarity degree [38] and a formal decision context [40], Mi et.…”

Section: Concept-cognitive Computingmentioning

confidence: 99%

“…Since the cognitive concept learning proposed by Li et al [12,13,42] mainly focuses on the lower and upper approximation from a given concept, it is called as an approximate concept-cognitive learning (CCL). Fig.…”

Section: Approximate Concept-cognitive Learningmentioning

confidence: 99%

“…Cognitive computing (CC) is known as an artificial intelligence of computer system modeled on the human brain [18,28]. It has been researched by different aspects, such as memory [10,25], learning [3,9,13,42], language [22], thinking [30], objective [1,6] and problem solving [19,29,39].…”

Section: Introductionmentioning

confidence: 99%

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A generalized concept-cognitive learning: A machine learning viewpoint

Mi¹,

Shi²,

Li³

et al. 2018

Preprint

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Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL.

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Section: Concept-cognitive Computingmentioning

confidence: 99%

Section: Approximate Concept-cognitive Learningmentioning

confidence: 99%

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A generalized concept-cognitive learning: A machine learning viewpoint

Mi¹,

Shi²,

Li³

et al. 2018

Preprint

Self Cite

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“…Furthermore, the concept cognition and learning were used as the basis of rule extraction in complex systems [ 3 – 7 ]. Kumar et al [ 3 ], Mi et al [ 4 ] and Zhao et al [ 5 ] conducted a series of studies from the perspective of granular concept cognitive learning. Furthermore, in order to improve the efficiency and flexibility of concept learning, Li et al [ 6 ] explored concept learning through granular computing from the perspective of cognitive computing.…”

Section: Introductionmentioning

confidence: 99%

Network rule extraction under the network formal context based on three-way decision

Min

2022

Appl Intell

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Knowledge discovery combined with network structure is an emerging field of network data analysis and mining. Three-way concept analysis is a method that can fit the human mind in uncertain decisions and analysis. In reality, when three-way concept analysis is placed in the background of a network, not only the three-way rules need to be obtained, but also the network characteristic values of these rules should be obtained, which is of great significance for concept cognition in the network. This paper mainly combines complex network analysis with the formal context of three-way decision. Firstly, the network formal context of three-way decision (NFC3WD) is proposed to unify the two studies mentioned above into one data framework. Then, the network weaken-concepts of three-way decision (NWC3WD) and their corresponding sub-networks are studied. Therefore, we can not only find out the network weaken-concepts but also know the average influence of the sub-network, as well as the influence difference within the sub-network. Furthermore, the concept logic of network and the properties of its operators are put forward, which lays a foundation for designing the algorithm of rule extraction. Subsequently, the bidirectional rule extraction algorithm and reduction algorithm based on confidence degree are also explored. Meanwhile, these algorithms are applied to the diagnosis examples of COVID-19 from which we can not only get diagnostic rules, but also know the importance of the population corresponding to these diagnostic rules in the network through network eigenvalues. Finally, experimental analysis is made to show the superiority of the proposed method.

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“…Therefore, formal concepts are the mathematization of philosophical concepts. As a practical tool for knowledge discovery, FCA has been successfully used in several areas, for instance data mining, information retrieval, social network analysis and machine learning [2,3,4,5,6]. In addition, some natural generalizations of derivation operators were proposed which induces some notions, for example, object-oriented concepts, property-oriented concepts, formal fuzzy concepts and three-way concepts [7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction and Rule Acquisition of Formal Decision Context Based on Dual Concept Lattice

Qin

2021

Learning Technologies and Systems

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This paper mainly studies the rule acquisition and attribute reduction for formal decision context based on two new kinds of decision rules, namely I-decision rules and II-decision rules. The premises of these rules are object-oriented concepts, and the conclusions are formal concept and property-oriented concept respectively. The rule acquisition algorithms for I-decision rules and II-decision rules are presented. Some comparative analysis of these algorithms with the existing algorithms are examined which shows that the algorithms presented in this study behave well. The attribute reduction approaches to preserve I-decision rules and II-decision rules are presented by using discernibility matrix.

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Cognitive concept learning from incomplete information

Cited by 43 publications

References 50 publications

A generalized concept-cognitive learning: A machine learning viewpoint

A generalized concept-cognitive learning: A machine learning viewpoint

Network rule extraction under the network formal context based on three-way decision

Attribute Reduction and Rule Acquisition of Formal Decision Context Based on Dual Concept Lattice

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