TY - GEN
T1 - Parallel implementation in case-based reasoning bankruptcy prediction system
AU - Rahayu, Dyah Sulistyowati
AU - Suhartanto, Heru
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - One of the strategies for enhancing the high performance computing in machine learning is by implementing the parallel computation, both multi-cores in Central Processing Unit (CPU) or multi-processor in Graphics Processing Unit (GPU). That evaluation is measured by how much the computation time is consumed. Case-based Reasoning (CBR) is a data historical based prediction algorithm which has satisfactory results but has low performance at the computation time. Due to the enormous financial historical data, the original CBR seems slow. This research shows how to speed up the computational time by parallelizing CBR calculations while maintaining accuracy in a bankruptcy prediction system. The computation time ratio of original sequential CBR, multi-cores CPU and multi-processor GPU is about 540:74:1. The experimental outputs prove that the strategy of parallelization success without reducing much of the classification performance. The parallel-CBR algorithm has 81% of accuracy.
AB - One of the strategies for enhancing the high performance computing in machine learning is by implementing the parallel computation, both multi-cores in Central Processing Unit (CPU) or multi-processor in Graphics Processing Unit (GPU). That evaluation is measured by how much the computation time is consumed. Case-based Reasoning (CBR) is a data historical based prediction algorithm which has satisfactory results but has low performance at the computation time. Due to the enormous financial historical data, the original CBR seems slow. This research shows how to speed up the computational time by parallelizing CBR calculations while maintaining accuracy in a bankruptcy prediction system. The computation time ratio of original sequential CBR, multi-cores CPU and multi-processor GPU is about 540:74:1. The experimental outputs prove that the strategy of parallelization success without reducing much of the classification performance. The parallel-CBR algorithm has 81% of accuracy.
KW - Bankruptcy prediction system
KW - Case-based reasoning
KW - Graphic processing unit
KW - High performance computing
KW - Multi-processor
UR - http://www.scopus.com/inward/record.url?scp=85099763179&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS51025.2020.9263170
DO - 10.1109/ICACSIS51025.2020.9263170
M3 - Conference contribution
AN - SCOPUS:85099763179
T3 - 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
SP - 269
EP - 274
BT - 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
Y2 - 17 October 2020 through 18 October 2020
ER -