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Hybrid case‑base maintenance approach for modeling large scale case‑based reasoning systems
Khan, M.J. Hayat, H. Awan, Irfan U.
Khan, M.J.
Hayat, H.
Awan, Irfan U.
Publication Date
2019
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© 2019 Springer. This work is licensed under a Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/
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Abstract
Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable
to continuously learn from the past experience. Each newly solved problem and its
corresponding solution is retained in its central knowledge repository called case-base.
Withρ the regular use of the CBR system, the case-base cardinality keeps on growing.
It results into performance bottleneck as the number of comparisons of each new
problem with the existing problems also increases with the case-base growth. To
address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising
on the utility of knowledge maintained in the case-base. This research work presents
a hybrid case-base maintenance approach which equally utilizes the benefits of case
addition as well as case deletion strategies to maintain the case-base in online and
offline modes respectively. The proposed maintenance method has been evaluated
using a simulated model of autonomic forest fire application and its performance has
been compared with the existing approaches on a large case-base of the simulated
case study.
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Citation
Khan MJ, Hayat H and Awan I (2019) Hybrid case‑base maintenance approach for modeling large scale case‑based reasoning
systems. Human-centric Computing and Information Sciences. 9(9).
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Article