{
  "id": "2106.07296",
  "version": "v1",
  "published": "2021-06-14T10:42:12.000Z",
  "updated": "2021-06-14T10:42:12.000Z",
  "title": "RRULES: An improvement of the RULES rule-based classifier",
  "authors": [
    "Rafel Palliser-Sans"
  ],
  "comment": "6 pages, 2 algorithms",
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "RRULES is presented as an improvement and optimization over RULES, a simple inductive learning algorithm for extracting IF-THEN rules from a set of training examples. RRULES optimizes the algorithm by implementing a more effective mechanism to detect irrelevant rules, at the same time that checks the stopping conditions more often. This results in a more compact rule set containing more general rules which prevent overfitting the training set and obtain a higher test accuracy. Moreover, the results show that RRULES outperforms the original algorithm by reducing the coverage rate up to a factor of 7 while running twice or three times faster consistently over several datasets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-06-14T10:42:12.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "rules rule-based classifier",
      "improvement",
      "detect irrelevant rules",
      "higher test accuracy",
      "extracting if-then rules"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}