Case-based learning

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.

All instance based learners have three properties:

1. They are lazy learners.
2. Classification is different for each instances.
3. Instance are represented with n-dimensional euclidean space. 

In CBR everything is considered as case and based on previous case, we propose a solution. Instances are represented as symbols not values.

CBR has three components:

1. Similarity function or distance measure.
2. Approximation/Adjustment of instances.
3. Symbolic representation of instances.

For modeling CBR we use CADET(Case Based Designing Tool) system. It has 75 predefined libraries.

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.

Example: Modern Water Taps

A problem specification: Water faucet

Function:

Ct= Control Temperature        Cq= Control Flow                Qc= Cold Water 

Qh= Hot Water                       Tc= Cool Temperature        Tm= Temperature Medium              

Th= Temperature High            Tn= Temperature Normal

Ques. Write short note on case-based learning algorithm.

Answer:

Case based format encourages active learning and demonstrates how to apply theoretical concepts to surgical practice. 

1. Can be an element of curriculum
2. Based on issue(s) that arise in a clinical case
3. Self-directed or structured
4. Structure depends on the level of the learner

Case based learning instruction is one of the artist oriented teaching approaches since it promotes students’ active participation so they could form their own learning. It helps transfer knowledge and expectations of the students from their learning.

It is often defined as a teaching method which requires students to actively participate in real or hypothetical problem situations, reflectingthe kinds of experiences naturally encountered in the discipline under study.

Cases are stories with a message which students analyze and consider the solutions of these stories. 

 

Ques. What are the functions of case-based learning algorithm?

Answer:

Functions of case-based learning algorithm are as follows:

1. Preprocessor:This prepares the input for processing e.g normalizing the range of numeric value features to ensure that they are treated with equal importance by the similarity function formatting the raw input into a set of cases etc 
2. Similarity:This function assesses the similarities of a given case with the previously stored cases in the concept description.Assessment may involve explicit encoding andor dynamic computation most practical CBL similarity functions and a compromise along the continuum between these extremes.
3. Prediction:This function inputs the similarity assessments and generates a prediction for the value of the given cases goal feature i.e a classification when it is symbolic values.
4. Memory Updating:This updates the stored casebase such as by modifying or abstracting previously stored cases forgetting cases presumed to be noisy or updating a features relevance weight setting

Ques. Describe a case-based learning cycle with different schemes of CBL.

Answer:

1. Case retrieval: After the problem situation has been assessed, the best matching case is searched in the case base and an approximate solution is retrieved. 

2. Case adaptation: The retrieved solution is adapted to fit better the new problem. 

3. Solution evaluation:The adapted solution can be evaluated either before the solution is applied to the problem or after the solution has been applied. In any case, if the accomplished result is not satisfactory, the retrieved solution must be adapted again or more cases should be retrieved.

4. Case-base updating: If the solution was verified as correct, the new case may be added to the case base.

Terms used in Cycle: 

A new problem is matched against the cases furnishing the case base and one or more similar cases are Retrieved.

A solution suggested by the matching cases is then Reused.

Unless the retrieved case is a close match, the solution will probably have to be Revised and tested for success, producing a new case that can be Retained ensuing, consequently, update of the case base. 

 

Ques. What are the benefits of CBL as a lazy problems solving method ? 

Answer:

The benefits of CBR as a lazy problem-solving method are: 

• Ease of knowledge elicitation
• Absence of problem-solving bias
• Incremental learning
• Suitability for complex and not-fully formalised solution spaces
• Suitability for sequential problem solving
• Ease of explanation
• Ease of maintenance

Ques. What are the limitations of CBL?

Answer:

The Limitations of Case Based Learning are as follows:

• Handling large case bases
• Dynamic problem domains
• Handling noisy data
• Fully automatic operation

Ques. What are the applications of CBL?

Answer:

Applications of  Case Based Learning are:

• Advising as a process of resolving diagnosed problems
• Design as a process of satisfying a number of posed constraints 
• Planning as a process of arranging a sequence of actions in time
• Interpretation as a process of evaluating situations/problems in some context
• Classification as a process of explaining a number of encountered symptoms

Ques. What are major paradigms of machine learning?

Answer:

Major Paradigms of Machine Learning include:

• Rote Learning: It deals with One-to-one mapping from inputs to stored representation. "Learning by memorization"  Association-based storage and retrieval.
• Induction:It uses specific examples to reach general conclusions
• Clustering:It involves automatically discovering natural grouping in data. 
• Analogy:Helps to determine correspondence between two different representations
• Discovery: It is a type of unsupervised learning in which a specific goal/outcome is not provided.
• Genetic Algorithms:It is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution.
• Reinforcement Learning:Only feedback (positive or negative reward) is  given at the end of a sequence of steps.Requires assigning reward to steps by solving the credit assignment problem involving to answer which steps should receive credit or blame for a final result?

Ques. Briefly explain the inductive learning problem.

Answer:

The Inductive Learning Problem

• Extrapolate from a given set of examples so that we can make accurate predictions about future examples.
• Supervised vs Unsupervised learning
  Want to learn an unknown function f(x) = y, where x is an input example and y is the desired output. Supervised learning implies we are given a set of (x, y) pairs by a "teacher." Unsupervised learning means we are only given the xs. In either case, the goal is to estimate f.
• Concept learning
  Given a set of examples of some concept/class/category, determine if a given example is an instance of the concept or not. If it is an instance, we call it a positive example. If it is not, it is called a negative example.
• Problem Example
  Supervised Concept Learning by Induction
  Given a training set of positive and negative examples of a concept, construct a description that will accurately classify whether future examples are positive or negative. That is, learn some good estimate of function f given a training set {(x1, y1), (x2, y2), ..., (xn, yn)} where each yi is either + (positive) or - (negative).