Machine learning is one of the most important subfields of artificial intelligence. It has been viewed as a viable way of avoiding the knowledge bottleneck problem in developing knowledge-based systems.

Inductive Learning, also known as Concept Learning, is how AI systems attempt to use a generalized rule to carry out observations.

To generate a set of classification rules, Inductive Learning Algorithms (APIs) are used. These generated rules are in the “If this then that” format.

These rules determine the state of an entity at each iteration step in Learning and how the Learning can be effectively changed by adding more rules to the existing ruleset.

When the output and examples of the function are fed into the AI system, inductive Learning attempts to learn the function for new data.

The Fundamental Concept of Inductive Learning

There are two methods for obtaining knowledge in the real world: first, from domain experts, and second, from machine learning.

Domain experts are not very useful or reliable for large amounts of data. As a result, for this project, we are adopting a machine learning approach.

The other method, using machine learning, replicates the logic of ‘experts’ in algorithms, but this work may be very complex, time-consuming, and expensive.

As a result, an option is the inductive algorithms, which generate a strategy for performing a task without requiring instruction at each step.

According to Jason Brownlee in his article “Basic Concepts in Machine Learning,” an excellent method to understand how Inductive Learning works is, for example, if we are given input samples (x) and output samples (f(x)) from the perspective of inductive Learning, and the problem is to estimate the function (f).

It is necessary then to generalize from the samples and the mapping so that it can be used to estimate the output for new samples in the future.

In practice, estimating the function is almost always too complicated, so we seek excellent approximations.

Some practical examples of induction are:

Credit risk assessment.

• The x is the property of the customer.
• The f(x) is credit approved or not.

Disease diagnosis.

• The x is the characteristics of a given patient.
• The f(x) is the patient’s disease.

Face recognition.

• The x are bitmaps of the faces we want to recognize.
• The f(x) is a name assigned to that face.

Automatic steering (autonomous driving).

• The x is bitmap images from a camera in front of the car.
• The f(x) is the degree to which the steering wheel should be turned.

Application

There are some situations in which inductive Learning is not a good idea. It is critical to understand when and when not to use supervised machine learning.

Inductive Learning may be helpful in the following four situations:

• Problems in which no human expertise is available. People cannot write a program to solve a problem if they do not know the answer. These are areas ripe for exploration.
• Humans can complete the task, but no one knows how to do it. There are situations in which humans can do things that computers cannot or do not do well. Riding a bike or driving a car are two examples.
• Problems where the desired function is frequently changing. Humans could describe it and write a program to solve it, but the problem changes too frequently. It is not economical. The stock market is one example.
• Problems where each user requires a unique function. Writing a custom program for each user is not cost-effective. Consider Netflix or Amazon recommendations for movies or books.

During the last years, there has been an increase in the amount of research on inductive Learning and its applications to various domains.

Concept learning is the most common application of inductive Learning.

The goal of concept learning is to find symbolic descriptions expressed in high-level terms that people can understand.

The following is a definition of concept learning:

• A set of (positive and negative) examples of a concept is provided and possibly some background knowledge.
• A general description (hypothesis) of the concept describes all of the positive examples but none of the negative ones.

MARBLE, a knowledge-based artificial intelligence (AI) system, has been developed to assess the riskiness of business loan applicants.

The paper “Applying inductive learning to enhance knowledge-based expert systems” describes the use of inductive Learning in MARBLE, a knowledge-based expert system developed to aid in business loan evaluation. MARBLE is unique in that it can learn.

Thus, the AI system employs inference rules to simulate a lending officer’s thought process when evaluating a loan request, generating loan-granting decision rules based on historical and proforma financial data.

This paper presents a learning method for inducing decision rules from training examples.

In the paper “Inductive learning for risk classification,” the authors discuss the application of inductive Learning to credit risk analysis, a similar domain application.

The authors address three risk classification issues:

• Business loan evaluation
• Bond-rating
• Prediction of Bankruptcy

It is discussed how to use the previously mentioned Marble system, a knowledge-based decision-support system that employs approximately 80 decision rules to evaluate commercial loans.

The paper describes an aspect of Marble that uses inductive Learning to classify financial risks, and it discusses the technique’s effectiveness.

Another example we can find in the paper “On the Application of Inductive Machine Learning Tools to Geographical Analysis” discusses the role of inductive machine learning in geographical analysis.

The presented discussion is not based on comparative results or mathematical descriptions, but rather is based on how the various inductive learning approaches differ operationally, describing:

• How the feature space is partitioned or clustered.
• To find suitable solutions, search mechanisms are used.
• The various biases imposed by each technique.

When considering complex geographic feature spaces, the consequences of these issues are then detailed.

The overall goal is to provide the basis for developing reliable inductive analysis methods rather than relying on piecemeal or haphazard experimentation with the various operational criteria that inductive learning tools require.

Inductive Learning has also been used in education. For example, because global education allows students to obtain education from multiple education providers through various study exchange programs, it necessitates comparing available study courses in foreign institutions to courses on the institution’s curriculum that issue the degree.

This issue necessitates a manual course comparison, which can be time-consuming and necessitates highly skilled experts to avoid the comparison, resulting in a superficial intuitive judgment and, as a result, course incompatibility issues.

The study, “Interactive Inductive Learning: Application in Domain of Education.” presents a technique for using Interactive inductive Learning supported by enterprise modeling as a way to support mechanisms that can help save time and effort in study course comparison.

Because inductive learning algorithms are domain-independent, they can be used in any classification or pattern recognition task.

Several applications of the rules family of induction algorithms to visual inspection are presented in the paper entitled “Applications of Inductive Learning to Automated Visual Inspection.”

The primary value of using Inductive Learning for visual inspection, according to authors Mehmet Sabih Aksoy, Orhan Torkul, Abdullah Almudimigh, and Ismail H. Cedimoglu:

• The systems have no orientation issues, which are critical in digital image processing.
• Because rules represent the pattern, it is not necessary to store it in graphics form in memory. Memory space is saved as a result of this.
• Because the number of conditions in each rule and the total number of rules is insignificant, the decision can be reached quickly.
• Because these systems are not very complex, it is simpler to develop software and design hardware for them.

Conclusion

In cases where traditional statistical approaches fail due to scalability and flexibility issues, Inductive Machine Learning tools, like neural networks and decision trees, provide alternative methods for classification, clustering, and pattern recognition that, in theory, can extend to the complex or “deep” data sets that pervade geography.

Inductive learning algorithms are domain-agnostic, and they can be applied in any task that requires classification or pattern recognition.

There has been an increase in the amount of research on inductive Learning and its applications to various domains in recent years. Several good algorithms have emerged as a result of this research. It is worth keeping an eye on.