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ML | Case Based Reasoning (CBR) Classifier
  • Last Updated : 26 Mar, 2020

As we know Nearest Neighbour classifiers stores training tuples as points in Euclidean space. But Case-Based Reasoning classifiers (CBR) use a database of problem solutions to solve new problems. It stores the tuples or cases for problem-solving as complex symbolic descriptions.

How CBR works?
When a new case arrises to classify, a Case-based Reasoner(CBR) will first check if an identical training case exists. If one is found, then the accompanying solution to that case is returned. If no identical case is found, then the CBR will search for training cases having components that are similar to those of the new case. Conceptually, these training cases may be considered as neighbours of the new case. If cases are represented as graphs, this involves searching for subgraphs that are similar to subgraphs within the new case. The CBR tries to combine the solutions of the neighbouring training cases to propose a solution for the new case. If compatibilities arise with the individual solutions, then backtracking to search for other solutions may be necessary. The CBR may employ background knowledge and problem-solving strategies to propose a feasible solution.

Applications of CBR includes:

  1. Problem resolution for customer service help desks, where cases describe product-related diagnostic problems.
  2. It is also applied to areas such as engineering and law, where cases are either technical designs or legal rulings, respectively.
  3. Medical educations, where patient case histories and treatments are used to help diagnose and treat new patients.

Challenges with CBR

  • Finding a good similarity metric (eg for matching subgraphs) and suitable methods for combining solutions.
  • Selecting salient features for indexing training cases and the development of efficient indexing techniques.

CBR becomes more intelligent as the number of the trade-off between accuracy and efficiency evolves as the number of stored cases becomes very large. But after a certain point, the system’s efficiency will suffer as the time required to search for and process relevant cases increases.

machine-learning

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