Constructive Induction On Decision Trees
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Machine Learning. Pages 645-649.
Abstract
Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically; unfortunately feature construction is a difficult and poorly understood problem . In this paper we present a definition of feature construction in concept learning, and offer a framework for its study based on four aspects: detection, selection, generalization, and evaluation. This framework is used in the analysis of existing learning systems and as the basis for the design of a new system, CITRE. CITRE performs feature construction using decision trees and simple domain knowledge as constructive biases. Initial results on a set of spatial-dependent problems suggest the importance of domain knowledge and feature generalization, i.e., constructive induction.
@inproceedings{ijcai1989p103,
title = {Constructive Induction On Decision Trees},
author = {Matheus, Christopher J. and Rendell, Larry A.},
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 = {645--649},
year = {1989},
month = {8},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1989/103},
}
TY - CPAPER TI - Constructive Induction On Decision Trees AU - Matheus, Christopher J. AU - Rendell, Larry A. T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 645 EP - 649 UR - https://ijcai.org/proceedings/1989/103 ER -Copied
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