What is Concept Learning in ML

Concept Learning in Machine Learning can be thought of as a boolean-valued function defined over a large set of training data. In my previous article I covered the basics of designing a learning system in ML, in order to complete the design of a learning algorithm, we need a learning mechanism or a good representation of the target concept.

Taking a very simple example, one possible target concept may be to Find the day when my friend Ramesh enjoys his favorite sport. We have some attributes/features of the day like, Sky, Air Temperature, Humidity, Wind, Water, Forecast and based on this we have a target Concept named EnjoySport.

We have the following training example available:

Example	Sky	AirTemp	Humidity	Wind	Water	Forecast	EnjoySport
1	Sunny	Warm	Normal	Strong	Warm	Same	Yes
2	Sunny	Warm	High	Strong	Warm	Same	Yes
3	Rainy	Cold	High	Strong	Warm	Change	No
4	Sunny	Warm	High	Strong	Cool	Change	Yes

Let’s Design the problem formally with TPE(Task, Performance, Experience):

Problem: Leaning the day when Ramesh enjoys the sport.

Task T: Learn to predict the value of EnjoySport for an arbitrary day, based on the values of the attributes of the day.

Performance measure P: Total percent of days (EnjoySport) correctly predicted.

Training experience E: A set of days with given labels (EnjoySport: Yes/No)

Let us take a very simple hypothesis representation which consists of a conjunction of constraints in the instance attributes. We get a hypothesis h_i with the help of example i for our training set as below:

hi(x) := <x1, x2, x3, x4, x5, x6>

where x1, x2, x3, x4, x5 and x6 are the values of Sky, AirTemp, Humidity, Wind, Water and Forecast.

Hence h1 will look like(the first row of the table above):

h1(x=1): <Sunny, Warm, Normal, Strong, Warm, Same >

Note: x=1 represents a positive hypothesis / Positive example

We want to find the most suitable hypothesis which can represent the concept. For example, Ramesh enjoys his favorite sport only on cold days with high humidity (This seems independent of the values of the other attributes present in the training examples).

h(x=1) = <?, Cold, High, ?, ?, ?>

Here ? indicates that any value of the attribute is acceptable.

Note: The most generic hypothesis will be < ?, ?, ?, ?, ?, ?> where every day is a positive example and the most specific hypothesis will be <?,?,?,?,?,? > where no day is a positive example.

We will discuss the two most popular approaches to find a suitable hypothesis, they are:

1. Find-S Algorithm

2. List-Then-Eliminate Algorithm

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Find-S Algorithm:

Following are the steps for the Find-S algorithm:

1. Initialize h to the most specific hypothesis in H

2. For each positive training example,

   1. For each attribute, constraint ai in h

      1. If the constraints ai is satisfied by x

      2. Then do nothing

      3. Else replace ai in h by the next more general constraint that is satisfied by x

3. Output hypothesis h

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The LIST-THEN-ELIMINATE Algorithm:

Following are the steps for the LIST-THE-ELIMINATE algorithm:

VersionSpace <- a list containing every hypothesis in H

For each training example, <x, c(x)>

• Remove from VersionSpace any hypothesis h for which h(x) != c(x)

Output the list of hypotheses in VersionSpace.

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Conclusion

The most suitable way to find a good hypothesis will be to start with both the directions, by taking the most general and the most specific boundaries. This approach is called a CANDIDATE-ELIMINATION Learning Algorithm. We will solve some nice examples in the next blog with all the help of the mentioned algorithms.

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• Introduction to Machine Learning Concepts

• What is Concept Learning in ML

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