TY - GEN
T1 - Trade Data Harmonization
T2 - 16th International Joint Conference on Computational Intelligence, IJCCI 2024
AU - Khargharia, Himadri Sikhar
AU - Shakya, Sid
AU - Ruta, Dymitr
N1 - Publisher Copyright:
© 2024 by SCITEPRESS – Science and Technology Publications, Lda.
PY - 2024
Y1 - 2024
N2 - Aligning trade data from disparate sources poses challenges due to volume disparities and category naming variations. This study aims to harmonize subcategories from a secondary dataset with those of a primary dataset, focusing on aligning the number and combined volumes of subcategories. We employ a multiobjective optimization approach using Non-dominated Sorting Genetic Algorithm II (NSGA-II) to facilitate trade-off assessments and decision-making via Pareto fronts. NSGA-II’s performance is compared with singleobjective optimization techniques, including Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), Distribution Estimation using Markov Random Field (DEUM), and Simulated Annealing (SA). The comparative analysis highlights NSGA-II’s efficacy in managing trade data complexities and achieving optimal solutions, demonstrating the effectiveness of meta-heuristic approaches in this context.
AB - Aligning trade data from disparate sources poses challenges due to volume disparities and category naming variations. This study aims to harmonize subcategories from a secondary dataset with those of a primary dataset, focusing on aligning the number and combined volumes of subcategories. We employ a multiobjective optimization approach using Non-dominated Sorting Genetic Algorithm II (NSGA-II) to facilitate trade-off assessments and decision-making via Pareto fronts. NSGA-II’s performance is compared with singleobjective optimization techniques, including Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), Distribution Estimation using Markov Random Field (DEUM), and Simulated Annealing (SA). The comparative analysis highlights NSGA-II’s efficacy in managing trade data complexities and achieving optimal solutions, demonstrating the effectiveness of meta-heuristic approaches in this context.
KW - Distribution Estimation Using MRF and Simulated Annealing
KW - Genetic Algorithm
KW - Non-Dominated Sorting Genetic Algorithm II
KW - Population-Based Incremental Learning
KW - Trade Data Harmonisation
UR - https://www.scopus.com/pages/publications/85211438540
U2 - 10.5220/0013052500003837
DO - 10.5220/0013052500003837
M3 - Conference contribution
AN - SCOPUS:85211438540
SN - 9789897587214
T3 - International Joint Conference on Computational Intelligence
SP - 338
EP - 345
BT - Proceedings of the 16th International Joint Conference on Computational Intelligence, IJCCI 2024
A2 - Marcelloni, Francesco
A2 - Madani, Kurosh
A2 - van Stein, Niki
A2 - Joaquim, Joaquim
Y2 - 20 November 2024 through 22 November 2024
ER -