Learning DNF by Decision Trees

Pagallo, Giulia

Learning DNF by Decision Trees

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Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Machine Learning. Pages 639-643.

Abstract

We investigate the problem of learning DNF concepts from examples using decision trees as a concept description language. Due to the replication problem, DNF concepts do not always have a concise decision tree description when the tests at the nodes are limited to the initial attributes . However, the representational complexity may be overcome by using high level attributes as tests. We present a novel algorithm that modifies the initial bias determined by the primitive attributes by adaptively enlarging the attribute set with high level attributes. We show empirically that this algorithm outperforms a standard decision tree algorithm for learning small random DNF with and without noise, when the examples are drawn from the uniform distribution.

Giulia Pagallo. 1989. Learning DNF by Decision Trees. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89. IJCAI, 639–643. https://ijcai.org/proceedings/1989/102

@inproceedings{ijcai1989p102,
  title     = {Learning DNF by Decision Trees},
  author    = {Pagallo, Giulia},
  booktitle = {Proceedings of the Eleventh International Joint Conference on
               Artificial Intelligence, {IJCAI-89}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {N. S. Sridharan},
  pages     = {639--643},
  year      = {1989},
  month     = {8},
  note      = {Machine Learning},
  url       = {https://ijcai.org/proceedings/1989/102},
}

TY  - CPAPER
TI  - Learning DNF by Decision Trees
AU  - Pagallo, Giulia
T2  - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1989
SP  - 639
EP  - 643
UR  - https://ijcai.org/proceedings/1989/102
ER  -