AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The Integrated Weighted Granular-ball Rough Set (IWGBRS) model is proposed, which integrates multiple granular-ball splitting strategies to achieve more stable and reliable partitions and reduces the randomness in the granular-ball generation process and ensures more consistent and stable partitioning results.
As an effective mathematical framework for modeling fuzziness, inaccuracy, and uncertainty, neighborhood rough sets have been widely applied to attribute reduction and other data mining tasks. Existing neighborhood rough set methods generally rely on fixed neighborhood parameters to construct neighborhoods, which m...
Neighborhood rough sets (NRSs) have been widely recognized as an efficient tool for solving attribute reduction problems in numerical data. In constructing neighborhood information granules, many existing extensions introduce weighting mechanisms for conditional attributes to quantify their influence on the decision at...
A decision-making framework integrating weighted neighborhood rough sets (WNRS) and threshold-based ordinal regression and a novel structural proximity operator is introduced, offering a new perspective for structure-enhanced decision-making within the framework from limited information space to ordered state.
Experiments show that MFGB-DBSCAN achieves competitive clustering accuracy and efficiency compared with representative baselines, particularly on datasets with varying densities and complex structures.
To address dimensional reduction for continuous single-label and multi-label data, this paper proposes an improved variable precision rough set method based on maximal consistent blocks. We formulate dimensional reduction as an attribute reduction problem in continuous decision information systems, construct a distance...
A fuzzy granular-ball random vector functional link (GBFRVFL) framework that leverages granular-ball computing to abstract raw samples into adaptive granular balls and introduces two membership assignment schemes that enhance robustness to noise, outliers, class imbalance, and uncertainty in granular-ball distributions...
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