DECISION TREE INDUCTION FROM COUNTEREXAMPLES

Nicolas Cebron, Fabian Richter, Rainer Lienhart

2012

Abstract

While it is well accepted in human learning to learn from counterexamples or mistakes, classic machine learning algorithms still focus only on correctly labeled training examples.We replace this rigid paradigm by using complementary probabilities to describe the probability that a certain class does not occur. Based on the complementary probabilities, we design a decision tree algorithm that learns from counterexamples. In a classification problem with K classes, K 􀀀1 counterexamples correspond to one correctly labeled training example. We demonstrate that even when only a partial amount of counterexamples is available, we can still obtain good performance.

Paper Citation

in Harvard Style

Cebron N., Richter F. and Lienhart R. (2012). DECISION TREE INDUCTION FROM COUNTEREXAMPLES . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 525-528. DOI: 10.5220/0003730405250528

in Bibtex Style

@conference{icpram12,
author={Nicolas Cebron and Fabian Richter and Rainer Lienhart},
title={DECISION TREE INDUCTION FROM COUNTEREXAMPLES},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={525-528},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003730405250528},
isbn={978-989-8425-99-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - DECISION TREE INDUCTION FROM COUNTEREXAMPLES
SN - 978-989-8425-99-7
AU - Cebron N.
AU - Richter F.
AU - Lienhart R.
PY - 2012
SP - 525
EP - 528
DO - 10.5220/0003730405250528