A5004055512- Rough Sets and Fuzzy Logic
- Data Mining Algorithms and Applications
- Text and Document Classification Technologies
- Image Retrieval and Classification Techniques
- Gene expression and cancer classification
- Face and Expression Recognition
- Advanced Computational Techniques and Applications
- Imbalanced Data Classification Techniques
- Privacy-Preserving Technologies in Data
- Metaheuristic Optimization Algorithms Research
- Multi-Criteria Decision Making
- Machine Learning and Data Classification
- Advanced Decision-Making Techniques
- Extenics and Innovation Methods
- Computational Drug Discovery Methods
- Internet Traffic Analysis and Secure E-voting
- Machine Learning in Bioinformatics
- Energy Load and Power Forecasting
- Advanced Steganography and Watermarking Techniques
- Digital Media Forensic Detection
- Medical Image Segmentation Techniques
- Drug Transport and Resistance Mechanisms
- Privacy, Security, and Data Protection
- Evolutionary Algorithms and Applications
- Geoscience and Mining Technology
Tianjin University of Science and Technology
2023-2025
Qingdao University
2025
Affiliated Hospital of Qingdao University
2025
Magic Leap (United States)
2025
Henan Normal University
2014-2024
Wuhan College
2024
Wenhua College
2024
Tsinghua University
2018-2021
Zhangzhou Normal University
2021
Xinxiang University
2019-2020
For heterogeneous data sets containing numerical and symbolic feature values, selection based on fuzzy neighborhood multigranulation rough (FNMRS) is a very significant step to preprocess improve its classification performance. This article presents an FNMRS-based approach in decision systems. First, some concepts of are given, then the FNMRS model investigated construct uncertainty measures. Second, optimistic pessimistic models built by using lower upper approximations from algebra view,...
Recently, multilabel classification has generated considerable research interest. However, the high dimensionality of data incurs costs; moreover, in many real applications, a number labels training samples are randomly missed. Thus, can have great complexity and ambiguity, which means some feature selection methods exhibit poor robustness yield low prediction accuracy. To solve these issues, this article presents novel method based on fuzzy neighborhood rough sets (MFNRS) maximum relevance...
Abstract Multi-label feature selection, a crucial preprocessing step for multi-label classification, has been widely applied to data mining, artificial intelligence and other fields. However, most of the existing selection methods dealing with mixed have following problems: (1) These rarely consider importance features from multiple perspectives, which analyzes not comprehensive enough. (2) select subsets according positive region, while ignoring uncertainty implied by upper approximation....
P-glycoproteins (P-gp) actively transport a wide variety of chemicals out cells and function as drug efflux pumps that mediate multidrug resistance limit the efficacy many drugs. Methods for facilitating early elimination potential P-gp substrates are useful new discovery. A computational ensemble pharmacophore model has recently been used prediction with promising accuracy 63%. It is desirable to extend range beyond compounds covered by known models. For such purpose, machine learning...
Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties chemical agents. These employ a variety molecular descriptors to characterize structural physicochemical molecules. Some these are specifically designed study particular type or agents, their use other agents might generate noise affect accuracy statistical learning system. This work examines what extent reduction this can improve A feature selection method,...
For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and selection plays a significant role in improving performance of data. In this study, an effective hybrid method ReliefF Ant colony optimization (ACO) algorithm for is proposed. First, algorithm, average distance among k nearest or non-nearest neighbor samples are introduced to estimate difference samples, which distances between same class different classes defined, then it...
Many optimization problems have become increasingly complex, which promotes researches on the improvement of different algorithms. The monarch butterfly (MBO) algorithm has proven to be an effective tool solve various kinds problems. However, in basic MBO algorithm, search strategy easily falls into local optima, causing premature convergence and poor performance many complex To issues, this paper develops a novel based opposition‐based learning (OBL) random perturbation (RLP). Firstly, OBL...
Partial multilabel learning (PML) has attracted considerable interest from scholars. Most PML models construct objective functions and optimize target parameters, which add noise to the training process results in a poor classification effect. In addition, feature correlation is limited linear transformations while ignoring complex relationships among features. this study, we develop novel model with fuzzy neighborhood-based ball clustering kernel extreme machine (KELM). To reduce...