@misc{10481/99509,
year = {2023},
month = {9},
url = {https://hdl.handle.net/10481/99509},
abstract = {Classification based on association rules has proved
able to get high precision and deal with data noise in real
scenarios. However, very low support thresholds are used to
get these results, generating a great number of rules. In this
process, interesting and non-interesting itemsets are considered
for subsequent rule generation. In this paper, we propose a
new method to identify not only frequent but also interesting
itemsets, including an interestingness measure when pruning
candidate itemsets. With this approach, we reduce the number of
association rules in the final classifier and increase precision. We
combine it with an iterative approach, reducing the execution
time and complexity of the final rule set. We have tested the
proposal using a COVID19 patient database.},
publisher = {IEEE},
keywords = {CBA},
keywords = {Complexity reduction},
keywords = {Classification},
keywords = {Interest measure},
title = {Classification based on Association Rules: Complexity and Interestingness Guided Algorithm},
doi = {0.1109/ICECCME57830.2023.10253036},
author = {Molina Fernández, Carlos and Prados Suárez, María Belén},
}