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Selective Sampling for Nearest Neighbor Classifiers


Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov. Selective Sampling for Nearest Neighbor Classifiers. In The Proceedings of the Sixteenth National Confernce on Artificial Intelligence, 366-371 Orlando, Florida, 1999.


Abstract

In the passive, traditional, approach to learning, the information available to the learner is a set of classified examples, which are randomly drawn from the instance space. In many applications, however, the initial classification of the training set is a costly process, and an intelligently selection of training examples from unlabeled data is done by an active learner. This paper proposes a lookahead algorithm for example selection and addresses the problem of active learning in the context of nearest neighbor classifiers. The proposed approach relies on using a random field model for the example labeling, which implies a dynamic change of the label estimates during the sampling process. The proposed selective sampling algorithm was evaluated empirically on artificial and real data sets. The experiments show that the proposed method outperforms other methods in most cases.


Keywords: Active Learning
Secondary Keywords:
Online version:
Bibtex entry:
 @inproceedings{Lindenbaum:1999:SSN,
  Author = {Michael Lindenbaum and Shaul Markovitch and Dmitry Rusakov},
  Title = {Selective Sampling for Nearest Neighbor Classifiers},
  Year = {1999},
  Booktitle = {The Proceedings of the Sixteenth National Confernce on Artificial Intelligence},
  Pages = {366--371},
  Address = {Orlando, Florida},
  Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Lindenbaum-Markovitch-Rusakov-aaai1999.pdf},
  Keywords = {Active Learning},
  Secondary-keywords = {Selective Sampling, Lookahead, Value of Information},
  Abstract = {
    In the passive, traditional, approach to learning, the information
    available to the learner is a set of classified examples, which
    are randomly drawn from the instance space. In many applications,
    however, the initial classification of the training set is a
    costly process, and an intelligently selection of training
    examples from unlabeled data is done by an active learner. This
    paper proposes a lookahead algorithm for example selection and
    addresses the problem of active learning in the context of nearest
    neighbor classifiers. The proposed approach relies on using a
    random field model for the example labeling, which implies a
    dynamic change of the label estimates during the sampling process.
    The proposed selective sampling algorithm was evaluated
    empirically on artificial and real data sets. The experiments show
    that the proposed method outperforms other methods in most cases.
  }

  }