TY - JOUR
T1 - Optimization of Decision Trees with Hypotheses for Knowledge Representation
AU - Azad, Mohammad
AU - Chikalov, Igor
AU - Hussain, Shahid
AU - Moshkov, Mikhail
N1 - KAUST Repository Item: Exported on 2021-07-02
Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) including the provision of computing resources. The authors are greatly indebted to the anonymous reviewers for useful comments and suggestions.
PY - 2021/6/30
Y1 - 2021/6/30
N2 - In this paper, we consider decision trees that use two types of queries: queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to the ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth and number of nodes of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions. Decision trees with hypotheses generally have less complexity, i.e., they are more understandable and more suitable as a means for knowledge representation.
AB - In this paper, we consider decision trees that use two types of queries: queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to the ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth and number of nodes of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions. Decision trees with hypotheses generally have less complexity, i.e., they are more understandable and more suitable as a means for knowledge representation.
UR - http://hdl.handle.net/10754/669869
UR - https://www.mdpi.com/2079-9292/10/13/1580
U2 - 10.3390/electronics10131580
DO - 10.3390/electronics10131580
M3 - Article
SN - 2079-9292
VL - 10
SP - 1580
JO - Electronics
JF - Electronics
IS - 13
ER -