TY - JOUR
T1 - Facilitating innovation and knowledge transfer between homogeneous and heterogeneous datasets
T2 - Generic incremental transfer learning approach and multidisciplinary studies
AU - Chui, Kwok Tai
AU - Arya, Varsha
AU - Band, Shahab S.
AU - Alhalabi, Mobeen
AU - Liu, Ryan Wen
AU - Chi, Hao Ran
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/4/1
Y1 - 2023/4/1
N2 - Open datasets serve as facilitators for researchers to conduct research with ground truth data. Generally, datasets contain innovation and knowledge in the domains that could be transferred between homogeneous datasets and have become feasible using machine learning models with the advent of transfer learning algorithms. Research initiatives are drawn to the heterogeneous datasets if these could extract useful innovation and knowledge across datasets of different domains. A breakthrough can be achieved without the restriction requiring the similarities between datasets. A multiple incremental transfer learning is proposed to yield optimal results in the target model. A multiple rounds multiple incremental transfer learning with a negative transfer avoidance algorithm are proposed as a generic approach to transfer innovation and knowledge from the source domain to the target domain. Incremental learning has played an important role in lowering the risk of transferring unrelated information which reduces the performance of machine learning models. To evaluate the effectiveness of the proposed algorithm, multidisciplinary studies are carried out in 5 disciplines with 15 benchmark datasets. Each discipline comprises 3 datasets as studies with homogeneous datasets whereas heterogeneous datasets are formed between disciplines. The results reveal that the proposed algorithm enhances the average accuracy by 4.35% compared with existing works. Ablation studies are also conducted to analyse the contributions of the individual techniques of the proposed algorithm, namely, the multiple rounds strategy, incremental learning, and negative transfer avoidance algorithms. These techniques enhance the average accuracy of the machine learning model by 3.44%, 0.849%, and 4.26%, respectively.
AB - Open datasets serve as facilitators for researchers to conduct research with ground truth data. Generally, datasets contain innovation and knowledge in the domains that could be transferred between homogeneous datasets and have become feasible using machine learning models with the advent of transfer learning algorithms. Research initiatives are drawn to the heterogeneous datasets if these could extract useful innovation and knowledge across datasets of different domains. A breakthrough can be achieved without the restriction requiring the similarities between datasets. A multiple incremental transfer learning is proposed to yield optimal results in the target model. A multiple rounds multiple incremental transfer learning with a negative transfer avoidance algorithm are proposed as a generic approach to transfer innovation and knowledge from the source domain to the target domain. Incremental learning has played an important role in lowering the risk of transferring unrelated information which reduces the performance of machine learning models. To evaluate the effectiveness of the proposed algorithm, multidisciplinary studies are carried out in 5 disciplines with 15 benchmark datasets. Each discipline comprises 3 datasets as studies with homogeneous datasets whereas heterogeneous datasets are formed between disciplines. The results reveal that the proposed algorithm enhances the average accuracy by 4.35% compared with existing works. Ablation studies are also conducted to analyse the contributions of the individual techniques of the proposed algorithm, namely, the multiple rounds strategy, incremental learning, and negative transfer avoidance algorithms. These techniques enhance the average accuracy of the machine learning model by 3.44%, 0.849%, and 4.26%, respectively.
KW - Deep learning
KW - Domain knowledge
KW - Incremental learning
KW - Innovation transfer
KW - Knowledge transfer
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85149218500&partnerID=8YFLogxK
U2 - 10.1016/j.jik.2023.100313
DO - 10.1016/j.jik.2023.100313
M3 - Article
AN - SCOPUS:85149218500
SN - 2530-7614
VL - 8
JO - Journal of Innovation and Knowledge
JF - Journal of Innovation and Knowledge
IS - 2
M1 - 100313
ER -