{
  "id": "1312.4986",
  "version": "v1",
  "published": "2013-12-17T22:12:52.000Z",
  "updated": "2013-12-17T22:12:52.000Z",
  "title": "A Comparative Evaluation of Curriculum Learning with Filtering and Boosting",
  "authors": [
    "Michael R. Smith",
    "Tony Martinez"
  ],
  "comment": "19 pages, 2 figures, 6 tables",
  "categories": [
    "cs.LG"
  ],
  "abstract": "Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. Several machine learning techniques treat instances in a data set differently during training such as curriculum learning, filtering, and boosting. However, an automated method for determining how beneficial an instance is for inferring a model of the data does not exist. In this paper, we present an automated method that orders the instances in a data set by complexity based on the their likelihood of being misclassified (instance hardness). The underlying assumption of this method is that instances with a high likelihood of being misclassified represent more complex concepts in a data set. Ordering the instances in a data set allows a learning algorithm to focus on the most beneficial instances and ignore the detrimental ones. We compare ordering the instances in a data set in curriculum learning, filtering and boosting. We find that ordering the instances significantly increases classification accuracy and that filtering has the largest impact on classification accuracy. On a set of 52 data sets, ordering the instances increases the average accuracy from 81% to 84%.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2013-12-17T22:12:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "data set",
      "curriculum learning",
      "comparative evaluation",
      "machine learning techniques treat instances",
      "instances significantly increases classification accuracy"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 19,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1312.4986S"
    }
  }
}