6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule should correspond to a branch in the decision tree. 7. Rule Application: Apply your rule-based system to the following instance: Color: Green, Size: Small, Temperature: 24.0, Weight: 260 8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement. 9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization. Problem 1 Exercises: Rule-Based Color Size Temperature Weight Class Sample (Nominal) (Ordinal) (Continuous) (Discrete) (Target) 1 Red Large 25.5 350 Positive 2 Blue Small 18.0 200 Negative 3 Green Medium 22.3 280 Positive 4 Red Small 19.8 210 Negative 10 5 Blue Large 28.1 400 Positive 6 Green Medium 23.5 320 Positive 7 Red Large 26.8 370 Positive 8 Blue Small 17.2 180 Negative 9 Green Medium 21.0 250 Negative 10 Red Small 20.5 230 Positive 1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples into either the positive or negative class based on the given attributes (Color, Size, Temperature, Weight). 2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under which a sample is classified as positive or negative. 3. Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification. 4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how confident are you in classifying a sample as positive based on the rules? 5. Rule Expansion: Add a new rule to your rule-based system.
6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule should correspond to a branch in the decision tree. 7. Rule Application: Apply your rule-based system to the following instance: Color: Green, Size: Small, Temperature: 24.0, Weight: 260 8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement. 9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization. Problem 1 Exercises: Rule-Based Color Size Temperature Weight Class Sample (Nominal) (Ordinal) (Continuous) (Discrete) (Target) 1 Red Large 25.5 350 Positive 2 Blue Small 18.0 200 Negative 3 Green Medium 22.3 280 Positive 4 Red Small 19.8 210 Negative 10 5 Blue Large 28.1 400 Positive 6 Green Medium 23.5 320 Positive 7 Red Large 26.8 370 Positive 8 Blue Small 17.2 180 Negative 9 Green Medium 21.0 250 Negative 10 Red Small 20.5 230 Positive 1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples into either the positive or negative class based on the given attributes (Color, Size, Temperature, Weight). 2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under which a sample is classified as positive or negative. 3. Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification. 4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how confident are you in classifying a sample as positive based on the rules? 5. Rule Expansion: Add a new rule to your rule-based system.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
Rule based problem
![6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule
should correspond to a branch in the decision tree.
7. Rule Application: Apply your rule-based system to the following instance:
Color: Green, Size: Small, Temperature: 24.0, Weight: 260
8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the
reasoning behind the refinement.
9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader
set of samples. Explain the reasoning behind the generalization.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F10eebb53-c222-4ba4-91a2-e54084fe388d%2F5c935307-5829-46f3-a5e4-397f3b0fb41b%2Fcu60jmg_processed.png&w=3840&q=75)
Transcribed Image Text:6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule
should correspond to a branch in the decision tree.
7. Rule Application: Apply your rule-based system to the following instance:
Color: Green, Size: Small, Temperature: 24.0, Weight: 260
8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the
reasoning behind the refinement.
9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader
set of samples. Explain the reasoning behind the generalization.
![Problem 1
Exercises: Rule-Based
Color
Size
Temperature
Weight
Class
Sample
(Nominal)
(Ordinal)
(Continuous)
(Discrete)
(Target)
1
Red
Large
25.5
350
Positive
2
Blue
Small
18.0
200
Negative
3
Green
Medium
22.3
280
Positive
4
Red
Small
19.8
210
Negative
10
5
Blue
Large
28.1
400
Positive
6
Green
Medium
23.5
320
Positive
7
Red
Large
26.8
370
Positive
8
Blue
Small
17.2
180
Negative
9
Green
Medium
21.0
250
Negative
10
Red
Small
20.5
230
Positive
1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples
into either the positive or negative class based on the given attributes (Color, Size,
Temperature, Weight).
2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under
which a sample is classified as positive or negative.
3. Rule Modification: Modify one of the rules to observe how it affects the classification.
Explain the impact of the modification.
4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how
confident are you in classifying a sample as positive based on the rules?
5. Rule Expansion: Add a new rule to your rule-based system.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F10eebb53-c222-4ba4-91a2-e54084fe388d%2F5c935307-5829-46f3-a5e4-397f3b0fb41b%2F2c86ff_processed.png&w=3840&q=75)
Transcribed Image Text:Problem 1
Exercises: Rule-Based
Color
Size
Temperature
Weight
Class
Sample
(Nominal)
(Ordinal)
(Continuous)
(Discrete)
(Target)
1
Red
Large
25.5
350
Positive
2
Blue
Small
18.0
200
Negative
3
Green
Medium
22.3
280
Positive
4
Red
Small
19.8
210
Negative
10
5
Blue
Large
28.1
400
Positive
6
Green
Medium
23.5
320
Positive
7
Red
Large
26.8
370
Positive
8
Blue
Small
17.2
180
Negative
9
Green
Medium
21.0
250
Negative
10
Red
Small
20.5
230
Positive
1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples
into either the positive or negative class based on the given attributes (Color, Size,
Temperature, Weight).
2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under
which a sample is classified as positive or negative.
3. Rule Modification: Modify one of the rules to observe how it affects the classification.
Explain the impact of the modification.
4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how
confident are you in classifying a sample as positive based on the rules?
5. Rule Expansion: Add a new rule to your rule-based system.
Expert Solution
![](/static/compass_v2/shared-icons/check-mark.png)
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 2 steps with 6 images
![Blurred answer](/static/compass_v2/solution-images/blurred-answer.jpg)
Recommended textbooks for you
![Database System Concepts](https://www.bartleby.com/isbn_cover_images/9780078022159/9780078022159_smallCoverImage.jpg)
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
![Starting Out with Python (4th Edition)](https://www.bartleby.com/isbn_cover_images/9780134444321/9780134444321_smallCoverImage.gif)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
![Digital Fundamentals (11th Edition)](https://www.bartleby.com/isbn_cover_images/9780132737968/9780132737968_smallCoverImage.gif)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
![Database System Concepts](https://www.bartleby.com/isbn_cover_images/9780078022159/9780078022159_smallCoverImage.jpg)
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
![Starting Out with Python (4th Edition)](https://www.bartleby.com/isbn_cover_images/9780134444321/9780134444321_smallCoverImage.gif)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
![Digital Fundamentals (11th Edition)](https://www.bartleby.com/isbn_cover_images/9780132737968/9780132737968_smallCoverImage.gif)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
![C How to Program (8th Edition)](https://www.bartleby.com/isbn_cover_images/9780133976892/9780133976892_smallCoverImage.gif)
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
![Database Systems: Design, Implementation, & Manag…](https://www.bartleby.com/isbn_cover_images/9781337627900/9781337627900_smallCoverImage.gif)
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
![Programmable Logic Controllers](https://www.bartleby.com/isbn_cover_images/9780073373843/9780073373843_smallCoverImage.gif)
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education