Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Abstract

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]. In lazy machine learning methods, training of the model evolves with time and presentation of more data. As the new data is presented, it continues contributing in improvement of the learning curve of the system. CBR is implemented as a learning layer onto the problem domain. Each new problem instance which needs to be resolved through this learning method is presented as a case. It is matched with the cases present in the knowledge repository called case-base. Set of the nearest neighbors of a new problem is extracted using a selected similarity measure. Solutions of the retrieved nearest neighbors of the case at hand are combined together appropriately to figure out the new solution. It may undergo a fine tuning exercise if the need arises. Finally, the new case comprising of the problem and its relative solution is added into the existing 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.

Recurrent Neural Networks for Meteorological Time Series Imputation

“…Reasoning [10] are CBRi [11] and CBRm [8] which, like LANN, use only the prior and next values of an NA value, completing the missing values from the average of the historical data similar to the prior and next. The difference between the two is that CBRi is designed for short-gaps and CBRm for medium-gaps.…”

An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

“…In terms of case retention and case base maintenance, the typical approach is to directly add the newly-solved case into the case base, along with its solution [46]. Under the circumstance when the new case is extremely similar to a past case that has been already stored in the case base, a case forgetting strategy could be applied after evaluating the quality of both cases [47]. In our case, we have to pay attention to the association table as well, because the performance of our algorithm depends on this table.…”

CBRm: Case based Reasoning Approach for Imputation of Medium Gaps

“…CBR is a nature inspired problem solving methodology [18]. It uses a solution that worked for a problem to solve a similar new problem, it's called reasoning by remembering.…”

“…The first principle of the CBR approach is: similar problems have alike solutions i.e. to solve a new problem [18], the existing problems and their solutions from the case base are retrieved and re-used.…”

“…The second principle is that the type of problems which an agent faces tends to repeat [18]. Thus, there is similarity between past and current problems or current and future problems.…”