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USING 3 FILES breast-cancer.arff, diabetes.arff and iris.arff IN WEKA 3.8 Task 1: Classification performance evaluation [10...

USING 3 FILES breast-cancer.arff, diabetes.arff and iris.arff IN WEKA 3.8

Task 1: Classification performance evaluation [10 marks]

In this comparative analysis task, you are required to evaluate classification performance of five algorithms on three datasets using Weka.  Load breast-cancer.arff, diabetes.arff and iris.arff datasets into Weka one at a time and run each of the below algorithms with their default settings. Then, collect a 10-fold cross-validation classification results for quantitative evaluation.

a. MultilayerPerceptron

b. Naive Bayes

c. J48

d. RandomForest

e. RERTree

You need to write a report that shows performance comparison of these algorithms on the datasets. The report should contain quantitative comparison of classification accuracy in terms of the confusion matrix and other performance metrics used in Weka. Include necessary screenshots, tables, graphs, etc. to make your report comprehensive, and revealing insightful details on the performance comparison.

Solutions

Expert Solution

Comparative Classification Accuracy Using Weka

Datasets

Algorithm

breast-cancer.arff

diabetes.arff

iris.arf

Multilayer Perceptron

64.68%

75.39%

97.33%

Naïve Bayes

71.67%

76.30%

96.00%

J48

75.52%

73.82%

96.00%

Random Forest

69.58%

75.78%

95.33%

RERTree

70.62%

75.26%

94.00%

Detailed classification outcome: Test 1

Algorithm

Multilayer Perceptron

Dataset

breast-cancer.arff

Instances

286

Attributes

10

Correctly classified instances

185

Incorrectly classified instances

101

Confusion Matrix

a

b

Classified as

150

51

a = no-recurrence-event

50

35

b = recurrence-events

Detailed classification outcome: Test 2

Algorithm

Naïve Bayes

Dataset

breast-cancer.arff

Instances

286

Attributes

10

Correctly classified instances

205

Incorrectly classified instances

81

Confusion Matrix

a

b

Classified as

168

33

a = no-recurrence-event

48

37

b = recurrence-events

Detailed classification outcome: Test 3

Algorithm

J48

Dataset

breast-cancer.arff

Instances

286

Attributes

10

Correctly classified instances

216

Incorrectly classified instances

70

Confusion Matrix

a

b

Classified as

193

8

a = no-recurrence-event

62

23

b = recurrence-events

Detailed classification outcome: Test 4

Algorithm

Random Forest

Dataset

breast-cancer.arff

Instances

286

Attributes

10

Correctly classified instances

199

Incorrectly classified instances

87

Confusion Matrix

a

b

Classified as

175

26

a = no-recurrence-event

61

24

b = recurrence-events

Detailed classification outcome: Test 5

Algorithm

RERTree

Dataset

breast-cancer.arff

Instances

286

Attributes

10

Correctly classified instances

202

Incorrectly classified instances

84

Confusion Matrix

a

b

Classified as

183

18

a = no-recurrence-event

66

19

b = recurrence-events

Detailed classification outcome: Test 6

Algorithm

Multilayer Perceptron

Dataset

diabetes.arff

Instances

768

Attributes

9

Correctly classified instances

579

Incorrectly classified instances

189

Confusion Matrix

a

b

Classified as

416

84

a = tested negative

105

163

b = tested positive

Detailed classification outcome: Test 7

Algorithm

Naïve Bayes

Dataset

Diabetes.arff

Instances

768

Attributes

9

Correctly classified instances

586

Incorrectly classified instances

182

Confusion Matrix

a

b

Classified as

422

78

a = tested negative

104

164

b = tested positive

Detailed classification outcome: Test 8

Algorithm

J48

Dataset

Diabetes.arff

Instances

768

Attributes

9

Correctly classified instances

567

Incorrectly classified instances

201

Confusion Matrix

a

b

Classified as

407

93

a = tested negative

108

160

b = tested positive

Detailed classification outcome: Test 9

Algorithm

Random Forest

Dataset

Diabetes.arff

Instances

768

Attributes

9

Correctly classified instances

582

Incorrectly classified instances

186

Confusion Matrix

a

b

Classified as

418

82

a = tested negative

104

164

b = tested positive

Detailed classification outcome: Test 10

Algorithm

RERTree

Dataset

Diabetes.arff

Instances

768

Attributes

9

Correctly classified instances

578

Incorrectly classified instances

190

Confusion Matrix

a

b

Classified as

423

177

a = tested negative

113

155

b = tested positive

Detailed classification outcome: Test 11

Algorithm

Multilayer Perceptron

Dataset

iris.arf

Instances

150

Attributes

5

Correctly classified instances

146

Incorrectly classified instances

4

Confusion Matrix

a

b

c

Classified as

50

0

0

a = Iris- setosa

0

48

2

b = Iris- versicolor

0

2

48

c = Iris- virginica

Detailed classification outcome: Test 12

Algorithm

Naive Bayes Classifier

Dataset

iris.arf

Instances

150

Attributes

5

Correctly classified instances

144

Incorrectly classified instances

6

Confusion Matrix

a

b

c

Classified as

50

0

0

a = Iris- setosa

0

48

2

b = Iris- versicolor

0

4

46

c = Iris- virginica

Detailed classification outcome: Test 13

Algorithm

RandomForest

Dataset

iris.arf

Instances

150

Attributes

5

Correctly classified instances

143

Incorrectly classified instances

7

Confusion Matrix

a

b

c

Classified as

50

0

0

a = Iris- setosa

0

47

3

b = Iris- versicolor

0

4

46

c = Iris- virginica

Detailed classification outcome: Test 14

Algorithm

J48 pruned tree

Dataset

iris.arf

Instances

150

Attributes

5

Correctly classified instances

144

Incorrectly classified instances

6

Confusion Matrix

a

b

c

Classified as

49

1

0

a = Iris- setosa

0

47

3

b = Iris- versicolor

0

2

48

c = Iris- virginica

Detailed classification outcome: Test 15

Algorithm

RERTree

Dataset

iris.arf

Instances

150

Attributes

5

Correctly classified instances

141

Incorrectly classified instances

9

Confusion Matrix

a

b

c

Classified as

50

0

0

a = Iris- setosa

0

46

4

b = Iris- versicolor

0

5

45

c = Iris- virginica

venereology answered 2 years ago
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