{
  "id": "1610.04804",
  "version": "v1",
  "published": "2016-10-16T00:47:21.000Z",
  "updated": "2016-10-16T00:47:21.000Z",
  "title": "Dynamic Stacked Generalization for Node Classification on Networks",
  "authors": [
    "Zhen Han",
    "Alyson Wilson"
  ],
  "comment": "9 pages, 6 figures",
  "categories": [
    "stat.ML",
    "cs.LG",
    "cs.SI",
    "stat.AP"
  ],
  "abstract": "We propose a novel stacked generalization (stacking) method as a dynamic ensemble technique using a pool of heterogeneous classifiers for node label classification on networks. The proposed method assigns component models a set of functional coefficients, which can vary smoothly with certain topological features of a node. Compared to the traditional stacking model, the proposed method can dynamically adjust the weights of individual models as we move across the graph and provide a more versatile and significantly more accurate stacking model for label prediction on a network. We demonstrate the benefits of the proposed model using both a simulation study and real data analysis.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-10-16T00:47:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "dynamic stacked generalization",
      "node classification",
      "method assigns component models",
      "node label classification",
      "real data analysis"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 9,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}