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Found 19 papers, 2 papers with code
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection.
Multi-granularity Causal Structure Learning
Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines.
GBG++: A Fast and Stable Granular Ball Generation Method for Classification
Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary.
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
This mechanism inherently possesses an adaptive multi-granularity description capacity, resulting in computational traits such as efficiency, robustness, and interpretability.
Granular-ball Optimization Algorithm
The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.
Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls
In recent years, the problem of fuzzy clustering has been widely concerned.
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST.
Sketch Less Face Image Retrieval: A New Challenge
In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig. 1).
A novel cluster internal evaluation index based on hyper-balls
It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis.
Granular-Ball Fuzzy Set and Its Implementation in SVM
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing.
GBSVM: Granular-ball Support Vector Machine
Furthermore, a particle swarm optimization algorithm is designed to solve the dual model.
GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball
Existing clustering methods are based on a single granularity of information, such as the distance and density of each data.
One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning
Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene.
Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval
We consider that there is a significant correlation among these incomplete sketches in the sketch drawing episode of each photo.
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
In addition, this paper first provides the mathematical models for the granular-ball covering.
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two.
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time.
LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Based on the theorem, we propose the LRA framework for accelerating rough set algorithms.
Ball k-means
The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.
