Yes / 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. / Authors acknowledge the internal funding support received from Namal College Mianwali to complete the research work.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/16909 |
| Date | January 2019 |
| Creators | Khan, M.J., Hayat, H., Awan, Irfan U. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | © 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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