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Concept-cognitive learning survey: Mining and fusing knowledge from data
Abstract Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has
recently become a popular research subject in artificial intelligence and cognitive …
recently become a popular research subject in artificial intelligence and cognitive …
M-FCCL: Memory-based concept-cognitive learning for dynamic fuzzy data classification and knowledge fusion
Abstract Concept-cognitive learning (CCL) is an emerging field for studying the
representation and processing of knowledge embedded in data. Many efforts are focused on …
representation and processing of knowledge embedded in data. Many efforts are focused on …
Fuzzy-granular concept-cognitive learning via three-way decision: performance evaluation on dynamic knowledge discovery
Concept-cognitive learning (CCL) and three-way decision (3WD) models provide powerful
techniques for knowledge discovery. Some early attempts in the field have successfully …
techniques for knowledge discovery. Some early attempts in the field have successfully …
Two-way concept-cognitive learning via concept movement viewpoint
Representation and learning of concepts are critical problems in data science and cognitive
science. However, the existing research about concept learning has one prevalent …
science. However, the existing research about concept learning has one prevalent …
Fuzzy-based concept-cognitive learning: An investigation of novel approach to tumor diagnosis analysis
Medical decision-making with high-dimensional complex data has recently become a focus
and difficulty in artificial intelligence and the medical field. Tumor diagnosis using data …
and difficulty in artificial intelligence and the medical field. Tumor diagnosis using data …
Data-driven quantification and intelligent decision-making in traditional Chinese medicine: a review
X Chu, S Wu, B Sun, Q Huang - International Journal of Machine Learning …, 2024 - Springer
Traditional Chinese medicine (TCM) originates from the practical experience of human
beings' constant struggle with nature. In five thousand years, TCM has gradually risen from …
beings' constant struggle with nature. In five thousand years, TCM has gradually risen from …
Feature selection using zentropy-based uncertainty measure
Feature selection and entropy theory are two efficacious data analysis tools for investigating
uncertainty information processing in artificial intelligence. The fruitful marriage of the two …
uncertainty information processing in artificial intelligence. The fruitful marriage of the two …
Ze-HFS: Zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery
Knowledge discovery of heterogeneous data is an active topic in knowledge engineering.
Feature selection for heterogeneous data is an important part of effective data analysis …
Feature selection for heterogeneous data is an important part of effective data analysis …
A local rough set method for feature selection by variable precision composite measure
Feature selection using variable precision neighborhood rough sets (VPNRS) has garnered
considerable attention in data mining and knowledge discovery. Nevertheless, the positive …
considerable attention in data mining and knowledge discovery. Nevertheless, the positive …
Correlation concept-cognitive learning model for multi-label classification
J Wu, ECC Tsang, W Xu, C Zhang, L Yang - Knowledge-Based Systems, 2024 - Elsevier
As a cognitive process, concept-cognitive learning (CCL) emphasizes the structured
expression of data through systematic cognition and understanding, to obtain valuable …
expression of data through systematic cognition and understanding, to obtain valuable …