{
  "id": "2210.11675",
  "version": "v1",
  "published": "2022-10-21T02:03:52.000Z",
  "updated": "2022-10-21T02:03:52.000Z",
  "title": "Fuzzy Granular-Ball Computing Framework and Its Implementation in SVM",
  "authors": [
    "Shuyin Xia",
    "Xiaoyu Lian",
    "Yabin Shao"
  ],
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "Most existing fuzzy computing methods use points as input, which is the finest granularity from the perspective of granular computing. Consequently, these classifiers are neither efficient nor robust to label noise. Therefore, we propose a framework for a fuzzy granular-ball computational classifier by introducing granular-ball computing into fuzzy set. The computational framework is based on the granular-balls input rather than points; therefore, it is more efficient and robust than traditional fuzzy methods. Furthermore, the framework is extended to the fuzzy support vector machine (FSVM), and granular ball fuzzy SVM (GBFSVM) is derived. The experimental results demonstrate the effectiveness and efficiency of GBFSVM.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-10-21T02:03:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "fuzzy granular-ball computing framework",
      "implementation",
      "fuzzy support vector machine",
      "fuzzy granular-ball computational classifier",
      "granular ball fuzzy svm"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}