1. Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data
Source : Computers in Biology and Medicine, 2016, 73:38-46
Authors : Ali S, Majid A, Javed SG
Speaker : Jiefan Tan
Date : 2017/4/6
巴基斯坦应用和科学学院

2. Outline Introduction Related Work Proposed method Experiment
Conclusion

3. Introduction（1/3）
Traditional machine learning techniques assume that all classification errors have the same cost and try to minimize the number of errors rather than the total cost.
While in real-world applications, errors often have quite different cost. And there are a large number of imbalanced datasets in practical application.

4. Introduction（2/3）- Different cost
Traditional classification
Mental pressure
May die
Healthy
Cancer

5. Introduction（3/3） Dataset = * 1+ *99 overall accuracy rate = 99%
accuracy of the minority class = 0
classification
* 100
May die

6. Cost-Sensitive Algorithm
Related Work
Technology
Methods
代表算法
Rescaling
Thresholding
ETA
Sampling
BFKO,ADSNNHRS
Weighting
C4.5CS
Cost-Sensitive Algorithm
Cost-Sensitive Decision Tree
C4.5CS,CBDSDT
Cost-Sensitive Neural Networks
CSBNN,SDAE
Cost-Sensitive SVM
CSSVM,CISVM
Ensemble Algorithm
Bagging
Boosting
AdaBoost gentleBoost
Evaluation Criteria
Based on Cost Matrix
Precision,Recall,F-value,G-mean
Curve, Chart
ROC,AUC, Cost-Curve 

7. Proposed method(1/6)-Cost matrix
Cost matrix to evaluate two-class problem is shown in Table
In the table, we use the notation C(i,j) to represent the misclassification cost of classifying an instance from its actual class j into the predicted class i.
Actual negative
Actual positive
Predict negative
C(0,0), or TN
C(0,1), or FN
Predict positive
C(1,0), or FP
C(1,1), or TP

8. Proposed method(2/6)-CSC(1/2)
The expect cost R(i|x) of classifying an instance x into class i
(by a classifier) can be expressed as:
If P(j|x)>0.5 then x -> class j

9. Proposed method(3/6)-CSC(2/2)
The classifier will classify an instance x into positive class if and only if :

10. Proposed method(4/6)-Boosting
Training set …… ……
Boosting
Sample 1
Boosting
Sample K
Boosting
Sample T
Classifier 1
Classifier K
Classifier T
Boosting Ensemble Classifier
Voting results

11. Decision tree as base learners
Proposed method(5/6)
Extract Features
Decision tree as base learners
Ensemble Classifier

12. Proposed method(6/6)-Evaluation Criteria
Symbol
Calculate method
Precision
TP/(TP+FP)
Recall
(Sensitivity)Sp
TP/(TP+FN)
Specificity-Sn
TN/(TN+FP)
G-mean

13. Experiment

14. Conclusion
Imbalanced data classification problem has always been one of the important research issues in machine learning field.
The proposed Can-CSC-GBE system has effectively reduced the misclassification costs and thereby improved the overall classification performance.

15. Thank you !