TY - GEN
T1 - Improving Soybean Disease Prediction by Performing Late-Stage Re-Training Using Fireworks Algorithm
AU - Sandhu, Angad
AU - Krishna, Rajashree
AU - Gaur, Ishita
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper proposes a novel method of training and optimizing a model to get as optimal a result as possible. This study aims to shed light on alternative ways of using multiple optimizers with different properties to get better results, achieving an incremental increase in accuracy. This is done by switching the optimizer after training once, hence performing Late-Stage Re-Training (LSRT). The present study shows the use of 2 different optimizers, Adam and Fireworks, for their unique properties to gain better results. Adam is used first for most of the training process and is later switched out to Fireworks for re-training this model. This methodology is tested on the Soybean dataset. The above process leads to an average of 2-3% increase, thus obtaining a training accuracy of 98.9%.
AB - This paper proposes a novel method of training and optimizing a model to get as optimal a result as possible. This study aims to shed light on alternative ways of using multiple optimizers with different properties to get better results, achieving an incremental increase in accuracy. This is done by switching the optimizer after training once, hence performing Late-Stage Re-Training (LSRT). The present study shows the use of 2 different optimizers, Adam and Fireworks, for their unique properties to gain better results. Adam is used first for most of the training process and is later switched out to Fireworks for re-training this model. This methodology is tested on the Soybean dataset. The above process leads to an average of 2-3% increase, thus obtaining a training accuracy of 98.9%.
UR - http://www.scopus.com/inward/record.url?scp=85145355847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145355847&partnerID=8YFLogxK
U2 - 10.1109/DISCOVER55800.2022.9974674
DO - 10.1109/DISCOVER55800.2022.9974674
M3 - Conference contribution
AN - SCOPUS:85145355847
T3 - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
SP - 19
EP - 23
BT - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022
Y2 - 14 October 2022 through 15 October 2022
ER -