TY - GEN
T1 - Ant colony optimization of rough set for HV bushings fault detection
AU - Mpanza, L. J.
AU - Marwala, T.
PY - 2011
Y1 - 2011
N2 - In this paper we propose the optimization of Rough Set method using ant colony for oil-impregnated paper bushings. Ant colony is used to discretize the training data set. The ant colony optimized rough set is compare to a rough set who's data is discretized using equal frequency bin (EFB). Ant colony optimized (ACO) rough set results show an improvement compared to the EFB. The ACO rough set has an accuracy 4% high than that of EFB rough set. Rules generated are only a third for ACO compared to EFB. Although ACO takes longer to train, it proves to outperform EFB in all other respects.
AB - In this paper we propose the optimization of Rough Set method using ant colony for oil-impregnated paper bushings. Ant colony is used to discretize the training data set. The ant colony optimized rough set is compare to a rough set who's data is discretized using equal frequency bin (EFB). Ant colony optimized (ACO) rough set results show an improvement compared to the EFB. The ACO rough set has an accuracy 4% high than that of EFB rough set. Rules generated are only a third for ACO compared to EFB. Although ACO takes longer to train, it proves to outperform EFB in all other respects.
UR - http://www.scopus.com/inward/record.url?scp=84858755256&partnerID=8YFLogxK
U2 - 10.1109/IWACI.2011.6159982
DO - 10.1109/IWACI.2011.6159982
M3 - Conference contribution
AN - SCOPUS:84858755256
SN - 9781612843735
T3 - Proceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI 2011
SP - 97
EP - 102
BT - Proceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI 2011
T2 - 4th International Workshop on Advanced Computational Intelligence, IWACI 2011
Y2 - 19 October 2011 through 21 October 2011
ER -