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
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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.
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.
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.
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