Prediction rule discovery based on dynamic bias selection

Einoshin Suzuki, Toru Ohno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


This paper presents an algorithm for discovering prediction rules with dynamic bias selection. A prediction rule, which is aimed at predicting the class of an unseen example, deserves special attention due to its usefulness. However, little attention has been paid to the dynamic selection of biases in prediction rule discovery. A dynamic selection of biases is useful since it reduces humans’ burden of choosing and adjusting multiple mining algorithms. In this paper, we propose a novel rule discovery algorithm D3BiS, which is based on a data-driven criterion. Our approach has been validated using 17 data sets.

Original languageEnglish
Title of host publicationMethodologies for Knowledge Discovery and Data Mining - 3rd Pacific-Asia Conference, PAKDD 1999, Proceedings
EditorsLizhu Zhou, Ning Zhong
PublisherSpringer Verlag
Number of pages5
ISBN (Print)3540658661, 9783540658665
Publication statusPublished - 1999
Externally publishedYes
Event3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1999 - Beijing, China
Duration: Apr 26 1999Apr 28 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1999

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Prediction rule discovery based on dynamic bias selection'. Together they form a unique fingerprint.

Cite this