Tianxiang Gao

August 20, 2015

## Contents

• Motivation
• Introduction
• Methodology
• Experimental results
• Conclusion

## Motivation

“Learning from Imbalanced Data Sets,” Proc. Am. Assoc. for Artificial Intelligence (AAAI) Workshop, N. Japkowicz, ed., 2000, (Technical Report WS-00-05).

“Workshop Learning from Imbalanced Data Sets II,” International Conference on Machine Learning (ICML), N.V. Chawla, N. Japkowicz, and A. Kolcz, eds., 2003.

N.V. Chawla, N. Japkowicz, and A. Kolcz, “Editorial: Special Issue on Learning from Imbalanced Data Sets,” The ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Explorations Newsletter, vol. 6, no. 1, pp. 1-6, 2004.

## Contents

• Motivation
• Introduction
• Methodology
• Experimental results
• Conclusion

## Contents

• Motivation
• Introduction
• Classifier - Decision tree
• Evaluate performance
• Nature of problem
• Methodology
• Experimental results
• Conclusion

## Classifier

Classification problem is to correctly classifiy the previously unseen testing dataset based on the given training dataset. We deal with binary cases, positive class and negative class.

An algorithm that implements classification is known as a Classifier.

Decision tree is a tree-like classifier.

## Dataset of Playing Tennis

Outlook Temp. Humidity Windy Play
Sunny Hot High False No
Sunny Hot High True No
Overcase Hot High False Yes
... ... ... ... ...
Rainy Mild High True No

-Matt Tanner

## Contents

• Motivation
• Introduction
• Classifier - Decision tree
• Evaluate performace
• Nature of problem
• Methodology
• Experimental results
• Conclusion

## Why?

• Multiple classifiers are available
• For each classifier, multiple choices are available for settings
• To choose best classifier

## Cutoff value

Most algorithms classify via a 2-steps process:

1. Compute probability of belonging to $positive$ class.
2. Compare to cutoff value, and classify accordingly.

Default cutoff value is 0.5

• If >= 0.5, classify as $positive$.
• If < 0.5, classify as $negative$.

## Confusion Matrix

Predicted + Predicted -
Actual + True Positive (TP) False Negative (FN)
Actual - False Positive (FP) True Negative (TN)

## Accuracy

$ACC = \frac{TP+TN}{TP + TN + FN + FP}$

May not very useful if imbalanced datasets.

## TPR & FPR

True positive rate (TPR) $= \frac{TP}{TP + FN}$

False positive rate (FPR) $= \frac{FP}{FP + TN}$

## ROC curve

In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings.

-Wikipedia

## ROC & AUC

Area Under the ROC curve (AUC) is to quantify the overal performance of a classifier.

## Class Balance Accuracy

$CBA =\frac{\sum_{i=1}^{k} \frac{C_{ii}}{max(C_{i.}, C_{.i}) } }{k}$

where $C_{i.} = \sum_{j=1}^{k} C_{ij}$ and $C_{.i} = \sum_{i=1}^{k} C_{ji}$.

-L. Mosley and S. Olafsson 2013.

## Confusion Matrix

Predicted + Predicted -
Actual + True Positive (TP) False Negative (FN)
Actual - False Positive (FP) True Negative (TN)

## Contents

• Motivation
• Introduction
• Classifier - Decision tree
• Evaluate performance
• Nature of problem
• Methodology
• Experimental results
• Conclusion

## Nature of problem

Overlap, within-class imbalance, disjunct rules, authenticity of data.

## Contents

• Motivation
• Introduction
• Methodology
• Experimental results
• Conclusion

## Contents

• Motivation
• Introduction
• Methodology
• Sampling
• Instance Selection
• Hybrid method
• Experimental results
• Conclusion

## Sampling

• Undersampling
• Oversampling (with replacement)
• SMOTE
Under-sampling the majority class enables better classifiers to be built than over-sampling the minority class.

If replicate the minority class, the decision region for the minority class becomes very specific and will cause new splits in the decision tree...in essence, overfitting.

Replication of the minority class does not cause its decision boundary to spread into the majority class region.

- Chawla, Nitesh V., et al 2002.

## SMOTE

SMOTE stands for Synthetic Minority Over-sampling Technique

- Chawla, Nitesh V., et al 2002.

## SMOTE

- He, Haibo, Learning from imbalanced datasets 2009

Continuous - $x_{new}=x_i+(\hat{x}_i-x_i)\times \delta$

Categorical - $x_{new}=majorityVote(x_i)$

## Contents

• Motivation
• Introduction
• Methodology
• Sampling
• Instance Selection
• Hybrid method
• Experimental results
• Conclusion

## Instance Selection

Selects subset of training dataset such that removes superfluous instances, maintain performances.

## Greedy Selection

Greedy selection is a two-steps wrapper method:generates a number of candidate subsets, and starts with one candidate subset and continuouly combines the other subsets if combining improves the performance of classifier.

- W. Bennette and S. Olafsson 2013.

## Contents

• Motivation
• Introduction
• Methodology
• Sampling
• Instance Selection
• Hybrid method
• Experimental results
• Conclusion

## Hybrid Method

• Hyrid method is a combination method of SMOTE and greedy selection.

1. Generate synthetic instances for minority class, and combines those synthetic instances with majority instances.
2. Select the ideal subset from the SMOTEd instances by using greedy selection as the final training dataset.

## Contents

• Motivation
• Introduction
• Methodology
• Experimental results
• Characteristics of Datasets
• Results
• Conclusion

## Characteristics of Datasets

4 well-known imbalanced datasets in UCI machine learning repository, and one medical dataset

- Chawla, Nitesh V. 2002.

- Gang Wu 2003.

- Nathalie Japkowicz 2004.

- Haibo He 2009.

## Contents

• Motivation
• Introduction
• Methodology
• Experimental results
• Characteristics of Datasets
• Results
• Conclusion

## Results

Randomly select 4/5 of a dataset as original training dataset, and the rest is testing dataset.

Implement those strategies to preprocess the dataset and we got four different training datasets: Control, Greedy Selection, SMOTE, and Hybrid.

Fit those four different training datasets through regular decision tree.

Predict the test dataset.

Process these steps over 100 times among each dataset. Then, evaluate predications through computing AUC, CBA, and ACC.

## Contents

• Motivation
• Introduction
• Methodology
• Experimental results
• Conclusion

## Conclusion

• Hybrid Classification method makes decision tree works better.
• Robust
• Selecting an appropriate assessment metric is essential to wrapper-based method.
• Comprehensive assessment metric works better than non-comprehensive.