Generation of Attributes for Learning Algorithms

Hu, Yuh-Jyh; Kibler, Dennis F.

Generation of Attributes for Learning Algorithms

AAAI 1996 pp. 806-811

/aaai/1996/hu1996aaai-generation/

Abstract

Inductive algorithms rely strongly on their representational biases. Constructive induction can mitigate representational inadequacies. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA on artificial and real domains for several learners: C4.5, CN2, percept ron and backpropagation.

PDF AAAI Semantic Scholar

Cite

Text

Hu and Kibler. "Generation of Attributes for Learning Algorithms." AAAI Conference on Artificial Intelligence, 1996.

Markdown

[Hu and Kibler. "Generation of Attributes for Learning Algorithms." AAAI Conference on Artificial Intelligence, 1996.](https://mlanthology.org/aaai/1996/hu1996aaai-generation/)

BibTeX

@inproceedings{hu1996aaai-generation,
  title     = {{Generation of Attributes for Learning Algorithms}},
  author    = {Hu, Yuh-Jyh and Kibler, Dennis F.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1996},
  pages     = {806-811},
  url       = {https://mlanthology.org/aaai/1996/hu1996aaai-generation/}
}