Concept learning via granular computing: A cognitive viewpoint

Li, Jinhai; Mei, Changlin; Xu, Weihua; Qian, Yuhua

doi:10.1016/j.ins.2014.12.010

Cited by 262 publications

(58 citation statements)

References 48 publications

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“…Recently, the granular computing based concept lattice theory has received much attention [35]. Rough set theory, soft set theory and concept lattices have similar basis data description.…”

Section: F-soft Rough Sets and Modal-style Operators In Fcamentioning

confidence: 99%

A Comparative Study of Some Soft Rough Sets

2017

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Through the combination of different types of sets such as fuzzy sets, soft sets and rough sets, abundant hybrid models have been presented in order to take advantage of each other and handle uncertainties. A comparative study of relationships and interconnections of some existing hybrid models has been carried out. Some foundational properties of modified soft rough sets (MSR sets) are analyzed. It is pointed out that MSR approximation operators are some kinds of Pawlak approximation operators, whereas approximation operators of Z-soft rough fuzzy sets are equivalent to approximation operators of rough fuzzy sets. The relationships among F-soft rough fuzzy sets, M-soft rough fuzzy sets and Z-soft rough fuzzy sets are surveyed. A new model called soft rough soft sets has been provided as the generalization of F-soft rough sets, and its application in group decision-making has been studied. Various soft rough sets models show great potential as a tool to solve decision-making problems, and a depth study of the connections among these models contributes to the flexible application of soft rough sets based decision-making approaches.

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“…Recently, the granular computing based concept lattice theory has received much attention [35]. Rough set theory, soft set theory and concept lattices have similar basis data description.…”

Section: F-soft Rough Sets and Modal-style Operators In Fcamentioning

confidence: 99%

A Comparative Study of Some Soft Rough Sets

2017

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“…It covers multiple process modeling concepts of information processing in various hierarchical systems, as well as new approaches to learning with fuzzy databases [8] [9]. In this respect, the paradigm has common roots with the methods of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Classification Based on Invariants of the Data Matrix

Shats¹

2017

JILSA

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The paper proposes a solution to the problem classification by calculating the sequence of matrices of feature indices that approximate invariants of the data matrix. Here the feature index is the index of interval for feature values, and the number of intervals is a parameter. Objects with the equal indices form granules, including information granules, which correspond to the objects of the training sample of a certain class. From the ratios of the information granules lengths, we obtain the frequency intervals of any feature that are the same for the appropriate objects of the control sample. Then, for an arbitrary object, we find object probability estimation in each class and then the class of object that corresponds to the maximum probability. For a sequence of the parameter values, we find a converging sequence of error rates. An additional effect is created by the parameters aimed at increasing the data variety and compressing rare data. The high accuracy and stability of the results obtained using this method have been confirmed for nine data set from the UCI repository. The proposed method has obvious advantages over existing ones due to the algorithm's simplicity and universality, as well as the accuracy of the solutions.

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“…For instance, Xu et al [43] discussed how to obtain sufficient and necessary information granules from an arbitrary information granule. Li et al [14] put forward three cognitive concept learning methods from the perspectives of philosophy and cognitive psychology, and they [11] also designed a cognitive concept learning framework for big data. Moreover, the theory of three-way decisions has been incorporated into cognitive concept learning [15] as well.…”

Section: Introductionmentioning

confidence: 99%

“…Example 1 Table 1 depicts a dataset of four patients who suffer from severe acute respiratory syndrome (SARS) [14]. In the table, Fever, Cough, Headache and Difficulty breathing are four symptoms which were observed from the current patients.…”

Section: Introductionmentioning

confidence: 99%

Cognitive concept learning from incomplete information

Zhao

Liu

et al. 2016

Int. J. Mach. Learn. & Cyber.

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Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception, attention and thinking. In recent years, it has attracted much attention from the communities of formal concept analysis, cognitive computing and granular computing. However, the classical cognitive concept learning approaches are not suitable for incomplete information. Motivated by this problem, this study mainly focuses on cognitive concept learning from incomplete information. Specifically, we put forward a pair of approximate cognitive operators to derive concepts from incomplete information. Then, we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically. Moreover, cognitive processes are simulated based on three types of similarities. Finally, numerical experiments are conducted to evaluate the proposed cognitive concept learning methods.

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Concept learning via granular computing: A cognitive viewpoint

Cited by 262 publications

References 48 publications

A Comparative Study of Some Soft Rough Sets

A Comparative Study of Some Soft Rough Sets

Classification Based on Invariants of the Data Matrix

Cognitive concept learning from incomplete information

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