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.
Highlights
Case-based reasoning (CBR) is one of the widely used lazy machine learning methods inspired by natural learning behavior towards solving a new problem [1, 9, 22, 50]
Whenever a new problem is presented and its solution is derived by the CBR system, decision is made whether that completely resolved case should be retained in the case-base
Experiment 3: Empirical comparison in terms of performance Accuracy, recall and precision metrics computed for each case-base maintenance (CBM) technique have been compared with classical CBR cycle
Summary
Case-based reasoning (CBR) is one of the widely used lazy machine learning methods inspired by natural learning behavior towards solving a new problem [1, 9, 22, 50]. The time complexity for computing solution of a new problem increases and the overall performance of the decision support system gets compromised [28, 44] To address this performance bottleneck of a CBR system, the existing case-base needs to be maintained without compromising its problem solving capability [28]. In addition and deletion policies with conditional timing, the whole process of maintenance tends to recur quite often due to the blind addition of completely resolved case A hybrid case-base maintenance model has been proposed in this paper In this model, we have suggested a continuous timing addition policy as well as conditional timing deletion policy. Case‐base maintenance (CBM) Two important considerations play vital role in effectiveness of a CBR system [41]: 1. Case data, i.e., it is more complicated than to just learn from completely resolved case
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