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Лабораторная работа

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Министерство науки и образования РФ

Санкт-Петербургский Государственный Электротехнический Университет

Кафедра МОЭВМ

Отчёт по лабораторной работе № 1

по дисциплине

"Базы знаний и экспертные системы"

Вариант №1

Выполнил: Белов Д.А.

Группа : 3341

Санкт-Петербург

2006

  1. Описание входных данных

Вариант 1 , соответствует файлу : contact-lenses.arff

Данные из файла представляют собой информацию:

- о людях разных возрастов

- состоянии их зрения.

В таблице указан ряд параметров:

- возраст

- spectacle-prescrip

- астигматизм

- tear-prod-rate

- контактные линзы.

  1. Анализ задачи и выделение необходимого класса алгоритмов

Для анализа применяем алгоритмы классификации, так как единственным полезным знанием извлекаемым из этих данных, является построение классификации возрастов людей нуждающихся в контактных линзах

Из алгоритмов классификации выбраны построение правил и деревья.

  1. Результаты выполнения алгоритмов.

ID3

=== Run information ===

Scheme: weka.classifiers.trees.Id3

Relation: contact-lenses

Instances: 24

Attributes: 5

age

spectacle-prescrip

astigmatism

tear-prod-rate

contact-lenses

Test mode: 10-fold cross-validation

=== Classifier model (full training set) ===

Id3

tear-prod-rate = reduced: none

tear-prod-rate = normal

| astigmatism = no

| | age = young: soft

| | age = pre-presbyopic: soft

| | age = presbyopic

| | | spectacle-prescrip = myope: none

| | | spectacle-prescrip = hypermetrope: soft

| astigmatism = yes

| | spectacle-prescrip = myope: hard

| | spectacle-prescrip = hypermetrope

| | | age = young: hard

| | | age = pre-presbyopic: none

| | | age = presbyopic: none

Time taken to build model: 0 seconds

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 17 70.8333 %

Incorrectly Classified Instances 7 29.1667 %

Kappa statistic 0.4381

Mean absolute error 0.1944

Root mean squared error 0.441

Relative absolute error 51.4706 %

Root relative squared error 100.965 %

Total Number of Instances 24

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure Class

0.8 0.053 0.8 0.8 0.8 soft

0.25 0.1 0.333 0.25 0.286 hard

0.8 0.444 0.75 0.8 0.774 none

=== Confusion Matrix ===

a b c <-- classified as

4 0 1 | a = soft

0 1 3 | b = hard

1 2 12 | c = none

Naive Bayes

=== Run information ===

Scheme: weka.classifiers.bayes.NaiveBayes

Relation: contact-lenses

Instances: 24

Attributes: 5

age

spectacle-prescrip

astigmatism

tear-prod-rate

contact-lenses

Test mode: 10-fold cross-validation

=== Classifier model (full training set) ===

Naive Bayes Classifier

Class soft: Prior probability = 0.22

age: Discrete Estimator. Counts = 3 3 2 (Total = 8)

spectacle-prescrip: Discrete Estimator. Counts = 3 4 (Total = 7)

astigmatism: Discrete Estimator. Counts = 6 1 (Total = 7)

tear-prod-rate: Discrete Estimator. Counts = 1 6 (Total = 7)

Class hard: Prior probability = 0.19

age: Discrete Estimator. Counts = 3 2 2 (Total = 7)

spectacle-prescrip: Discrete Estimator. Counts = 4 2 (Total = 6)

astigmatism: Discrete Estimator. Counts = 1 5 (Total = 6)

tear-prod-rate: Discrete Estimator. Counts = 1 5 (Total = 6)

Class none: Prior probability = 0.59

age: Discrete Estimator. Counts = 5 6 7 (Total = 18)

spectacle-prescrip: Discrete Estimator. Counts = 8 9 (Total = 17)

astigmatism: Discrete Estimator. Counts = 8 9 (Total = 17)

tear-prod-rate: Discrete Estimator. Counts = 13 4 (Total = 17)

Time taken to build model: 0 seconds

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 17 70.8333 %

Incorrectly Classified Instances 7 29.1667 %

Kappa statistic 0.4381

Mean absolute error 0.2545

Root mean squared error 0.3326

Relative absolute error 67.3578 %

Root relative squared error 76.1544 %

Total Number of Instances 24

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure Class

0.8 0.053 0.8 0.8 0.8 soft

0.25 0.1 0.333 0.25 0.286 hard

0.8 0.444 0.75 0.8 0.774 none

=== Confusion Matrix ===

a b c <-- classified as

4 0 1 | a = soft

0 1 3 | b = hard

1 2 12 | c = none

LMT

=== Run information ===

Scheme: weka.classifiers.trees.LMT -I -1 -M 15

Relation: contact-lenses

Instances: 24

Attributes: 5

age

spectacle-prescrip

astigmatism

tear-prod-rate

contact-lenses

Test mode: 10-fold cross-validation

=== Classifier model (full training set) ===

Logistic model tree

------------------

: LM_1:36/36 (24)

Number of Leaves : 1

Size of the Tree : 1

LM_1:

Class 0 :

-0.05 +

[age=pre-presbyopic] * 2.86 +

[age=presbyopic] * -4.85 +

[spectacle-prescrip] * 9.59 +

[astigmatism] * -22.94 +

[tear-prod-rate] * 1.5

Class 1 :

-11.45 +

[age=young] * 3.05 +

[age=pre-presbyopic] * -1.57 +

[spectacle-prescrip] * -13.85 +

[astigmatism] * 17.09 +

[tear-prod-rate] * 2.36

Class 2 :

25.46 +

[age=young] * -8.13 +

[age=presbyopic] * 2.29 +

[tear-prod-rate] * -26.68

Time taken to build model: 0.2 seconds

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 17 70.8333 %

Incorrectly Classified Instances 7 29.1667 %

Kappa statistic 0.4766

Mean absolute error 0.2484

Root mean squared error 0.3748

Relative absolute error 65.7427 %

Root relative squared error 85.8137 %

Total Number of Instances 24

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure Class

0.8 0.053 0.8 0.8 0.8 soft

0.5 0.15 0.4 0.5 0.444 hard

0.733 0.333 0.786 0.733 0.759 none

=== Confusion Matrix ===

a b c <-- classified as

4 0 1 | a = soft

0 2 2 | b = hard

1 3 11 | c = none

J48

=== Run information ===

Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2

Relation: contact-lenses

Instances: 24

Attributes: 5

age

spectacle-prescrip

astigmatism

tear-prod-rate

contact-lenses

Test mode: 10-fold cross-validation

=== Classifier model (full training set) ===

J48 pruned tree

------------------

tear-prod-rate = reduced: none (12.0)

tear-prod-rate = normal

| astigmatism = no: soft (6.0/1.0)

| astigmatism = yes

| | spectacle-prescrip = myope: hard (3.0)

| | spectacle-prescrip = hypermetrope: none (3.0/1.0)

Number of Leaves : 4

Size of the tree : 7

Time taken to build model: 0.03 seconds

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 20 83.3333 %

Incorrectly Classified Instances 4 16.6667 %

Kappa statistic 0.71

Mean absolute error 0.15

Root mean squared error 0.3249

Relative absolute error 39.7059 %

Root relative squared error 74.3898 %

Total Number of Instances 24

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure Class

1 0.053 0.833 1 0.909 soft

0.75 0.1 0.6 0.75 0.667 hard

0.8 0.111 0.923 0.8 0.857 none

=== Confusion Matrix ===

a b c <-- classified as

5 0 0 | a = soft

0 3 1 | b = hard

1 2 12 | c = none

  1. Вывод

При выполнении работы были получены навыки применения алгоритмов DM для практического извлечения знаний из набора данных. Полученные результаты подтвердили правильность выбранных классов алгоритмов и подхода к поиску знаний.