Data Mining Analysis (breast-cancer data)
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1 Data Mining Analysis (breast-cancer data) Jung-Ying Wang Register number: D , May, 2003 Abstract In this AI term project, we compare some world renowned machine learning tools. Including WEKA data mining software (developed at the University of Waikato, Hamilton, New Zealand); MATLIB 6.1 and LIBSVM (developed at the Nation Taiwan University, by Chih-Jen Lin). Contents 1 Breast-cancer-Wisconsin dataset summary Classification - 10 fold cross validation on breast-cancer-wisconsin dataset Results for: Naive Bayes Results for: BP Neural Network Results for: J48 decision tree (implementation of C4.5) Results for: SMO (Support Vector Machine) Results for: JRip (implementation of the RIPPER rule learner) Classification Compare with other paper results Results for training data: Naive Bayes Results for test data: Naive Bayes Results for training data: BP Neural Network Results for test data: BP Neural Network Results for training data: J48 decision tree (implementation of C4.5) Results for test data: J48 decision tree (implementation of C4.5) Results for training data: J48 decision tree (implementation of C4.5) Results for training data: SMO (Support Vector Machine) Results for test data: SMO (Support Vector Machine) Results for training data: JRip (implementation of the RIPPER rule learner) Results for test data: JRip (implementation of the RIPPER rule learner) Summary Reference...26 D
2 1. Breast-cancer-Wisconsin dataset summary In our AI term project, all chosen machine learning tools will be use to diagnose cancer Wisconsin dataset. To be consistent with the literature [1, 2] we removed the 16 instances with missing values from the dataset to construct a new dataset with 683 instances. Brief information from the UC Irvine machine learning repository: Located in breast-cancer-wisconsin sub-directory, filenames root: breast-cancer-wisconsin. Currently contains 699 instances. 2 classes (malignant and benign). 9 integer-valued attributes. Attribute Information: Table 1 shows data attribute information # Attribute Domain 1. Sample code number id number 2. Clump Thickness Uniformity of Cell Size Uniformity of Cell Shape Marginal Adhesion Single Epithelial Cell Size Bare Nuclei Bland Chromatin Normal Nucleoli Mitoses Class 2 for benign, 4 for malignant Missing attribute values: 16 There are 16 instances in Groups 1 to 6 that contain a single missing (i.e., unavailable) attribute value, now denoted by "?". Class distribution: Benign: 458 (65.5%) Malignant: 241 (34.5%) D
3 Clump Thickness Freq Domain Figure 1: bar graph summaries for the clump thickness attributes in the training data. Uniformity of Cell Size Freq Domain Figure 2: Bar graph summaries for the Uniformity of Cell Size attributes in the training data D
4 Uniformity of Cell Shape Freq Domain Figure 3: Bar graph summaries for the Uniformity of Cell Shape attributes in the training data Marginal Adhesion Freq Domain Figure 4: Bar graph summaries for the Marginal Adhesion attributes in the training data D
5 Single Epithelial Cell Size Freq Domain Figure 5: Bar graph summaries for the Single Epithelial Cell Size attributes in the training data Bare Nuclei Freq Domain Figure 6: Bar graph summaries for the Bare Nuclei attributes in the training data D
6 Bland Chromatin Freq Domain Figure 7: Bar graph summaries for the Bland Chromatin attributes in the training data Normal Nucleoli Freq Domain Figure 8: Bar graph summaries for the Normal Nucleoli attributes in the training data D
7 Mitoses Freq Domain Figure 9: Bar graph summaries for the Mitoses attributes in the training data Table 2 shows data summary statistics. Domain Sum Clump Thickness Uniformity of Cell Size Uniformity of Cell Shape Marginal Adhesion Single Epithelial Cell Size Bare Nuclei Bare Nuclei Normal Nucleoli Mitoses Sum D
8 2 Classification - 10 fold cross validation on breast-cancer-wisconsin dataset First we use the data mining tools WEKA to do the training data prediction. In here, we will use 10 fold cross validation on training data to calculate the machine learning rules their performance. The results are as follows: 2.1 Results for: Naive Bayes Scheme: weka.classifiers.bayes.naivebayes Relation: breast Instances: 683 Test mode: 10-fold cross-validation Time taken to build model: 0.08 seconds === Stratified cross-validation === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
9 2.2 Results for: BP Neural Network Scheme: weka.classifiers.functions.neural.neuralnetwork -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a Relation: breast Instances: 683 Test mode: 10-fold cross-validation Time taken to build model: seconds === Stratified cross-validation === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
10 2.3 Results for: J48 decision tree (implementation of C4.5) Scheme: weka.classifiers.trees.j48.j48 -C M 2 Relation: breast Instances: 683 Test mode: 10-fold cross-validation J48 pruned tree a2 <= 2 a6 <= 3: 2 (395.0/2.0) a6 > 3 a1 <= 3: 2 (11.0) a1 > 3 a7 <= 2 a4 <= 3: 4 (2.0) a4 > 3: 2 (2.0) a7 > 2: 4 (8.0) a2 > 2 a3 <= 2 a1 <= 5: 2 (19.0/1.0) a1 > 5: 4 (4.0) a3 > 2 a2 <= 4 a6 <= 2 a4 <= 3: 2 (11.0/1.0) a4 > 3: 4 (3.0) a6 > 2: 4 (54.0/7.0) a2 > 4: 4 (174.0/3.0) Number of Leaves : 11 Size of the tree : 21 Time taken to build model: 0.08 seconds === Stratified cross-validation === D
11 Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for: SMO (Support Vector Machine) Scheme: weka.classifiers.functions.supportvector.smo -C 1.0 -E 1.0 -G A T P 1.0E-12 -N 0 -V -1 -W 1 Relation: breast Instances: 683 Test mode: 10-fold cross-validation SMO Classifier for classes: 2, 4 BinarySMO Machine linear: showing attribute weights, not support vectors * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a5 D
12 * (normalized) a * (normalized) a * (normalized) a * (normalized) a Number of kernel evaluations: Time taken to build model: 0.53 seconds === Stratified cross-validation === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for: JRip (implementation of the RIPPER rule learner) Scheme: weka.classifiers.rules.jrip -F 3 -N 2.0 -O 2 -S 1 Relation: breast Instances: 683 Test mode: 10-fold cross-validation JRIP rules: D
13 =========== (a2 >= 4) and (a7 >= 5) => class=4 (148.0/2.0) (a6 >= 3) and (a1 >= 7) => class=4 (50.0/0.0) (a3 >= 4) and (a4 >= 4) => class=4 (22.0/2.0) (a6 >= 4) and (a3 >= 3) => class=4 (19.0/5.0) (a7 >= 4) and (a1 >= 5) => class=4 (8.0/3.0) (a8 >= 3) and (a1 >= 6) => class=4 (2.0/0.0) => class=2 (434.0/2.0) Number of Rules : 7 Time taken to build model: 0.19 seconds === Stratified cross-validation === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
14 3. Classification Compare with other paper results Above machine learning tools will use in this section to diagnose cancer Wisconsin dataset. To be consistent with the literature [1, 2] we removed the 16 instances with missing values from the dataset to construct a dataset with 683 instances. The first 400 instances in the dataset are chosen as the training data, and the remaining 283 as the test data Results for training data: Naive Bayes Scheme: weka.classifiers.bayes.naivebayes Relation: breast_training Instances: 400 Test mode: evaluate on training data Naive Bayes Classifier Class 2: Prior probability = 0.57 Class 4: Prior probability = 0.43 Time taken to build model: 0.02 seconds === Evaluation on training set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
15 3.1.2 Results for test data: Naive Bayes Scheme: weka.classifiers.bayes.naivebayes Relation: breast_training Instances: 400 Test mode: user supplied test set: 283 instances Naive Bayes Classifier Class 2: Prior probability = 0.57 Class 4: Prior probability = 0.43 Time taken to build model: 0.02 seconds === Evaluation on test set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
16 3.2.1 Results for training data: BP Neural Network Scheme: weka.classifiers.functions.neural.neuralnetwork -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a Relation: breast_training Instances: 400 Test mode: evaluate on training data Time taken to build model: 4.74 seconds === Evaluation on training set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for test data: BP Neural Network Scheme: weka.classifiers.functions.neural.neuralnetwork -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a D
17 Relation: breast_training Instances: 400 Test mode: user supplied test set: 283 instances Time taken to build model: 5 seconds === Evaluation on test set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for training data: J48 decision tree (implementation of C4.5) Scheme: weka.classifiers.trees.j48.j48 -C M 2 Relation: breast_training Instances: 400 Test mode: evaluate on training data J48 pruned tree D
18 a3 <= 2 a7 <= 3: 2 (194.0/1.0) a7 > 3 a1 <= 4: 2 (7.0) a1 > 4: 4 (6.0/1.0) a3 > 2 a6 <= 2 a5 <= 4: 2 (20.0/1.0) a5 > 4: 4 (12.0) a6 > 2: 4 (161.0/9.0) Number of Leaves : 6 Size of the tree : 11 Time taken to build model: 0.02 seconds === Evaluation on training set === Correctly Classified Instances % Incorrectly Classified Instances 12 3 % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
19 3.3.2 Results for test data: J48 decision tree (implementation of C4.5) Scheme: weka.classifiers.trees.j48.j48 -C M 2 Relation: breast_training Instances: 400 Test mode: user supplied test set: 283 instances J48 pruned tree a3 <= 2 a7 <= 3: 2 (194.0/1.0) a7 > 3 a1 <= 4: 2 (7.0) a1 > 4: 4 (6.0/1.0) a3 > 2 a6 <= 2 a5 <= 4: 2 (20.0/1.0) a5 > 4: 4 (12.0) a6 > 2: 4 (161.0/9.0) Number of Leaves : 6 Size of the tree : 11 Time taken to build model: 0.02 seconds === Evaluation on test set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error D
20 Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for training data: SMO (Support Vector Machine) Scheme: weka.classifiers.functions.supportvector.smo -C 1.0 -E 1.0 -G A T P 1.0E-12 -N 0 -V -1 -W 1 Relation: breast_training Instances: 400 Test mode: evaluate on training data SMO Classifier for classes: 2, 4 BinarySMO Machine linear: showing attribute weights, not support vectors * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a Number of kernel evaluations: 7446 Time taken to build model: 0.66 seconds D
21 === Evaluation on training set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for test data: SMO (Support Vector Machine) Scheme: weka.classifiers.functions.supportvector.smo -C 1.0 -E 1.0 -G A T P 1.0E-12 -N 0 -V -1 -W 1 Relation: breast_training Instances: 400 Test mode: user supplied test set: 283 instances SMO Classifier for classes: 2, 4 BinarySMO Machine linear: showing attribute weights, not support vectors * (normalized) a1 D
22 * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a * (normalized) a Number of kernel evaluations: 7446 Time taken to build model: 0.33 seconds === Evaluation on test set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme 4296 bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for training data: JRip (implementation of the RIPPER rule learner) Scheme: weka.classifiers.rules.jrip -F 3 -N 2.0 -O 2 -S 1 Relation: breast_training Instances: 400 D
23 Test mode: evaluate on training data JRIP rules: =========== (a2 >= 3) and (a2 >= 5) => class=4 (116.0/2.0) (a6 >= 3) and (a3 >= 3) => class=4 (55.0/7.0) (a1 >= 6) and (a8 >= 4) => class=4 (5.0/0.0) (a2 >= 4) => class=4 (4.0/1.0) => class=2 (220.0/1.0) Number of Rules : 5 Time taken to build model: 0.05 seconds === Evaluation on training set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = Results for test data: JRip (implementation of the RIPPER rule learner) Scheme: weka.classifiers.rules.jrip -F 3 -N 2.0 -O 2 -S 1 Relation: breast_training Instances: 400 D
24 Test mode: user supplied test set: 283 instances JRIP rules: =========== (a2 >= 3) and (a2 >= 5) => class=4 (116.0/2.0) (a6 >= 3) and (a3 >= 3) => class=4 (55.0/7.0) (a1 >= 6) and (a8 >= 4) => class=4 (5.0/0.0) (a2 >= 4) => class=4 (4.0/1.0) => class=2 (220.0/1.0) Number of Rules : 5 Time taken to build model: 0.08 seconds === Evaluation on test set === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic K&B Relative Info Score % K&B Information Score bits bits/instance Class complexity order bits bits/instance Class complexity scheme bits bits/instance Complexity improvement (Sf) bits bits/instance Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances a = b = 4 D
25 4. Summary This section presents summary tables for scheme accuracy and running times. Table 4.1: Accuracy and running time summary table for 10 fold cross validation Model Running time 10 fold cross val. Naive Bayes 0.08 seconds % BP neural network seconds % J48 decision tree (C4.5) 0.08 seconds % SMO (support vector machine) 0.53 seconds % JRip (RIPPER rule learner) 0.19 seconds % Table 4.2: Accuracy for training and test data between different models Model Training data Test data Naive Bayes 95.50% % BP neural network 98.75% % J48 decision tree (C4.5) 97.00% % SMO (support vector machine) 96.75% % JRip (RIPPER rule learner) 97.25% % Table 4.2: A comparison with the others papers Method Testing Error WEKA Naive Bayes % BP neural network % J48 decision tree (C4.5) % SMO (support vector machine) % JRip (RIPPER rule learner) % Fogel et al. [1] % Abbass et. al. [2] % Abbass H. A. [3] % D
26 5. Reference 1. Fogel DB, Wasson EC, Boughton EM. Evolving neural networks for detecting breast cancer. Cancer lett 1995; 96(1): Abbass HA, Towaey M, Finn GD. C-net: a method for generating non-deterministic and dynamic multivariate decision trees. Knowledge Inf. Syst. 2001; 3: Abbass HA. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine 2002; 25, D
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