2024/07/03 by Jie Yang, Yang, Jie, Lingyun Xiaodiao +11
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Face and Expression Recognition #Image Processing and 3D Reconstruction #Image and Object Detection Techniques #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2407.11027
openalex publication_date 2024/07/03 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28
The granular-ball (GB)-based classifier introduced by Xia, exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and flexibility. Nevertheless, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. To solve this problem, we construct a robust three-way classifier with shadowed GBs for uncertain data. Firstly, combine with information entropy, we propose an enhanced GB generation method with the principle of justifiable granularity. Subsequently, based on minimum uncertainty, a shadowed mapping is utilized to partition a GB into Core region, Important region and Unessential region. Based on the constructed shadowed GBs, we establish a three-way classifier to categorize data instances into certain classes and uncertain case. Finally, extensive comparative experiments are conducted with 2 three-way classifiers, 3 state-of-the-art GB-based classifiers, and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our model demonstrates robustness in managing uncertain data and effectively mitigates classification risks. Furthermore, our model almost outperforms the other comparison methods in both effectiveness and efficiency.