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Introduction
In our day-to-day life, we all learn from our past experiences to move forward and handle situations that life throws at us. This is similar to something known as ‘Case-Based Learning,’ in which we address new problems by learning from past experiences. Do you know that CBR (Case-Based Learning) is widely used in AI and has a wide range of applications, from medicine to machine learning?
In this article, we will discuss about Case-Basd Reasoning in Machine learning. We will also discuss about CBR cycle, advantages, disadvantages, and applications of Case-Based Reasoning in machine learning.
So get ready to understand the fusion of Case-Base Reasoning in Machine Learning and untangle all your doubts.
Machine learning
Machine learning is a subdomain of AI (artificial intelligence) whose main aim is to develop models that allow the system to make a decision without human intervention. It involves designing systems that are able to learn from situations and improve further for efficient problem-solving and decision-making.
Therefore the main idea is to use build models that are able to perform pattern matching, make predictions, and solve high-level problems without the need to write code in bulk separately. Machine learning is closely associated with data mining and data science. It accepts data as its input and uses algorithms to give output.
Moving forward, let's understand case-based reasoning in machine learning.
Case-Based Reasoning in Machine Learning
In Case-based reasoning, a new problem is solved by adapting solutions that were also useful in the past. Therefore, it is also referred to as an experience-based approach/ intelligent problem-solving method. Therefore, it means learning from past experiences and using that knowledge to approach new problems.
For example, assume there is a CBR mechanism for an e-commerce application that provides services to its customer. The CBR mechanism can be used to improve customer experiences based on past experiences. Let’s say if someone likes a particular category, then the CBR mechanism helps find similar cases to the customer.
Types of Knowledge in case-based reasoning
There are mainly four types of knowledge containers in CBR.
Vocabulary: Vocabulary involves the process of determininginformation about attributes and parameters required for the selection of features that are used for determining cases.
Similarity Measures: Similarity measures mean analyzing similarities between cases and selecting efficient methods to deal with the problem. To choose the most efficient similar measure, we must have a complete understanding of the actual domain problem.
Adaptation knowledge: Adaptation knowledge means adapting the information needed to evaluate multiple stages in the CBR cycle. It tells how differences in problems affect the solution. It involves guiding a process to improve a solution and select those which fit better to the needs and constraints of the given problem.
Cases: Cases consist of information about a solved problem. They are the representation of stored knowledge of past experiences and are a fundamental part of the CBR cycle. Their content is determined on the basis of selected vocabulary.
CBR Cycle
The CBR (Case-Based Learning) cycle is an iterative process that tells us how a new problem is approached. It refers to collecting together past experiences and making use of relevant information. Let's discuss the main steps in the Case-based reasoning cycle.
New Problem: The CBR cycle starts when a new problem arrives.
Case retrieval: After properly analyzing the problem, the relevant information is extracted by comparing similar cases with the newly arrived problem. Therefore, we put together useful similar cases to solve the problem.
Case reuse: As a next step in the cycle, the past information is reused, and feasible solutions are selected from similar cases.
Reasoning results: It refers to applying the filtered information and solutions by analyzing previous similar cases to the present problem. In this step, we generally use algorithms and heuristics to narrow down to a solution.
Case revision: After applying the necessary information, the next step is to evaluate the output→judge its effectiveness →and produce feedback. Suppose the result’s quality is not up to the mark. In that case, the solution is modified according to the feedback, and an efficient solution is recorded for future similar cases like this.
Case retain: Now, this revised case with the present problem and the efficient solution is stored as the case base, thus enriching with more problem-solving capabilities.
Case-Based Reasoning VS Other Techniques
The case-based reasoning is an exceptionally well-designed technique that uses past experiences to improve its performance. Let's discuss how it differs from other techniques.
Rule-based systems
In rule-based reasoning, pre-defined rules are used for solving problems. Experts in the field design this set of rules. On the other hand, CBR is efficient in handling situations where the rules may not be efficient or are not present.
Decision trees
These are a type of algorithms are used for solving classification problems and widely used in Machine Learning and data mining. While CBR uses past experiences instead of creating a decision tree based on the given data.
Neural networks
These are machine learning algorithms that use past information to make predictions on the provided information. They are used in natural language processing, stock market predictions, intelligent searching, and image recognition and are inspired by a human’s brain structure. While CBR’s primary focus is learning from stored past experiences cases.
Advantages of Case-Based Reasoning
Below are the advantages of case-based reasoning in machine learning.
CBR helps in avoiding past mistakes.
CBR is efficient in adapting to new problems by making use of previous cases.
CBR considers small details, features, and information relevant to the problem for effective decision-making.
Case retention in CBR helps in expanding knowledge and improving the system.
Disadvantages of Case-based Reading
Below are the disadvantages of case-based reasoning in machine learning.
CBR can be time-consuming as it involves various stages of retrieving similar cases and arriving at the best solution.
CBR is a resource-intensive process it requires more computational power.
Scalability is also an issue in CBR. With the increase in the case base, the time needed for retrieving and adapting the cases also increases, affecting the overall efficiency.
CBR may give out incorrect solutions if the cases are not represented correctly.
Applications of Case-Based Reasoning
Below are the applications of case-based reasoning in machine learning
CBR is used infault diagnosis systems for resolving faults. CBR helps in providing guidance to diagnose faults.
CBR can be used for the designing process by using previous design cases that fit into a similar set of rules.
It is also efficient for planning a process using past experiences and arranging the action sequence.
CBR can be used in classifying tasks by analyzing past cases and features associated with them.
Frequently Asked Questions
What is case-based reasoning in machine learning?
In Case-based reasoning, a new problem is solved by adapting solutions that were also useful in the past. Therefore, it is also referred to as an experience-based approach/ intelligent problem-solvingmethod.
What are rule-based systems?
In rule-based reasoning, pre-defined rules are used for solving problems. Experts in the field design this set of rules. On the other hand, CBR is efficient in handling situations where the rules may not be efficient or are not present.
What is a CBR cycle?
The CBR cycle is an iterative process that tells us how a new problem is approached. It refers to collecting together past experiences and making use of relevant information.
What is the disadvantage of case-based reasoning?
CBR can be a time-consuming process involving various stages of retrieving similar cases and arriving at the best solution.CBR is a resource-intensive process requiring more computational power.
What is the difference between decision trees and case-based reasoning?
Decision tree algorithms are used for solving classification problems and are widely used in machine learning and data mining. While CBR uses past experiences instead of creating a decision tree based on the given data.
Conclusion
In this article, we have discussed case-based reasoning in machine learning. We have also discussed about CBR cycle, its advantages, disadvantages, and applications in machine learning. To enhance your machine learning knowledge, refer to the articles below.
You can read more such descriptive articles on our platform, Coding Ninjas Studio. You can also consider our Machine Learning Course to give your career an edge over others.
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