AI - Imbalanced data

• Data

• https://www.kaggle.com/mlg-ulb/creditcardfraud

• 30

• https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data

• 58

• https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset

• 24

• Metrics

• Confusion metrics, precision, recall, f1

• ROC

• AUC PR

• Maximize precision-recall curve for the less common class

• Normalized Gini Coefficient

• Business impact

• E.g. reducing false positive is more important than reducing false negative

• Algorithms

• Decision tree

• Random forest, xgboost, lightGBM

• from sklearn.ensemble import IsolationForest

• AutoEncoders

• Use latent representations as inputs for another model

• GAN

• https://www.kaggle.com/utkarshroy17/credit-card-fraud-detection-using-gan

• Resampling

• Over/up-sampling minority class

• Note

• Always split into test and train sets before trying oversampling techniques

• Options

• Adding more copies of the minority class

• Synthetic sampling

• from imblearn.over_sampling import SMOTE

• Adaptive sampling

• Borderline-SMOTE

• SMOTE 개선

• ADASYN (adaptive synthetic sampling)

• Under/down-sampling majority class

• Options

• Removing examples from the majority class

• Tomek links

• from imblearn.under_sampling import TomekLinks

• Cluster centroids

• from imblearn.under_sampling import ClusterCentroids

• Over-sampling followed by under-sampling

• Options

• SMOTE and Tomek links

• from imblearn.combine import SMOTETomek

• Stratified sampling

• Removing the variance of the proportion inside batches

• Can be useful when batch training a classifier

• Outliers

• Interquartile Range (IQR)

• We calculate this by the difference between the 75th percentile and the 25th percentile. Our aim is to create a threshold beyond the 75th and 25th percentile that in case some instance passes this threshold the instance will be deleted.

• Features

• Major class와 minor class의 데이터가 잘 분리되여 있으면 imbalace하여도 문제 없음

• from sklearn.manifold import TSNE 

• from sklearn.decomposition import PCA, TruncatedSVD

• Adding

• If classes were not well separable: maybe can we find a new additional feature that can help distinguish between the two classes and, so, improve the classifier accuracy

• Compared to the approaches mentioned in the previous subsection that suggest changing the reality of data, this approach that consists of enriching data with more information from reality is a far better idea when it is possible.

• Deleting

• Drop all of the features that have very similar distributions between the two types of transactions

• Hyperparameters

• Classification threshold

• Use the Precision-Recall curve to choose the threshold

• Cross-validation

• from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline

• Resampling happens during CV