A5043215329- Rough Sets and Fuzzy Logic
- Data Stream Mining Techniques
- Neural Networks and Applications
- Image Retrieval and Classification Techniques
- Cognitive Computing and Networks
- Anomaly Detection Techniques and Applications
- Machine Learning and Algorithms
- Educational Technology and Pedagogy
- Imbalanced Data Classification Techniques
- Advanced Computational Techniques and Applications
- Advanced Decision-Making Techniques
- MRI in cancer diagnosis
- Text and Document Classification Technologies
- Ideological and Political Education
- Food Industry and Aquatic Biology
- Marine Bivalve and Aquaculture Studies
- Agricultural risk and resilience
- Educational Technology and Assessment
- Artificial Immune Systems Applications
- Radiomics and Machine Learning in Medical Imaging
- Knowledge Management and Technology
- Financial Distress and Bankruptcy Prediction
- Network Security and Intrusion Detection
- Extenics and Innovation Methods
- COVID-19 Pandemic Impacts
Central South University
2020-2025
University of Chinese Academy of Sciences
2019-2023
Kunming University of Science and Technology
2023
Beijing Institute of Big Data Research
2019-2023
Chinese Academy of Sciences
2018-2023
Dalian Polytechnic University
2012
Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering for concept classification discovery. However, the standard CCL algorithms are incapable of tackling continuous data directly, some methods mainly focus on attribute information, ignoring object information that is also important to improve analysis ability. Therefore, this article, we present a novel method, called fuzzy-based model (FCLM), address these two issues by exploiting hierarchical relations...
Concept-cognitive learning (CCL) is an emerging field of concerning incremental concept and dynamic knowledge processing in the context environments. Although CCL has been widely researched theory, existing studies have one problem: concepts obtained by systems do not generalization ability. In meantime, algorithms still face some challenges that: 1) classifiers to adapt gradually 2) previously acquired should be efficiently utilized. To address these problems, based on advantage that can...
In human concept learning, people can naturally combine a handful of labeled data with abundant unlabeled when they make classification decisions, which is also known as semi-supervised learning (SSL) in machine learning. Especially, not only static process cognition but vary gradually dynamic environments. Nevertheless, the classical SSL algorithms must be redesigned to accommodate newly input data. this sense, concept-cognitive may good choice, it implement processes by imitating cognitive...
Dynamic stream learning, which emphasizes high-velocity, single-pass, real-time responses to arriving data, is revealing new challenges the standard machine learning paradigm. In particular, existing (deep) neural networks perform poorly when on data streams, as they often require having access a large amount of training data. Therefore, address limitations in high-speed streams with stationary environment, we propose novel dynamic network, called Concept Neural Network (ConceptNN), by...
<p>People can often acquire knowledge dynamically and rapidly from different types of data, yet existing incremental learning algorithms are still computationally time consuming most stream methods mainly designed for streaming data while ignoring other data. Hence, this paper proposes a novel dynamic concept (CL) algorithm by imitating human cognitive processes the perspective brain logical cognition, which is named concept-cognitive computing system (streamC3S). For streamC3S, it...
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 cognitive computing. However, relationship among (CC), concept-cognitive (CCC), CCL model (CCLM) not clearly described. To this end, we first explain CC, CCC, CCLM. Then, propose generalized (GCCL) point view machine learning. Finally, experiments on some data sets are conducted to verify feasibility formation process GCCL.
<p>People can often acquire knowledge dynamically and rapidly from different types of data, yet existing incremental learning algorithms are still computationally time consuming most stream methods mainly designed for streaming data while ignoring other data. Hence, this paper proposes a novel dynamic concept (CL) algorithm by imitating human cognitive processes the perspective brain logical cognition, which is named concept-cognitive computing system (streamC3S). For streamC3S, it...