Abstract
Effective data reduction is mandatory for modeling complex domains. The work described here demonstrates how to use a symbolic classifier algorithm from machine learning to effectively reduce large amounts of data. The algorithm, Quirdan's ID3, uses input data records and corresponding classifications to produce a decision tree. The resulting tree can be used to classify previously unseen inputs. Alternatively, the attributes found in the tree can be used as the basis to develop other system modeling techniques such as neural networks or mathematical programming algorithms. This approach has been used to effectively reduce data from a large complex domain. The example shown here comes from the F/A-18 Hornet aircraft. Results of using the algorithm to identify different phases of flight from aircraft flight data is presented.
Recommended Citation
B. Fiachsbart et al., "Using the ID3 Symbolic Classification Algorithm to Reduce Data Density," Proceedings of the ACM Symposium on Applied Computing, pp. 292 - 296, Association for Computing Machinery, Apr 1994.
The definitive version is available at https://doi.org/10.1145/326619.326750
Department(s)
Mathematics and Statistics
Keywords and Phrases
Classification; ID3; Learning; Modeling
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
06 Apr 1994
