This document discusses using anomaly detection techniques to identify minority classes without labels. It summarizes using anomaly detection on an unlabeled cancer biopsy dataset to identify malignant biopsies as anomalies without knowing their labels. This "minority report" approach is well-suited for large unlabeled datasets where an adversarial minority class is expected, like credit card fraud or network intrusions, as it can identify outliers without predefined labels of what to look for.

1. Minority Report:
Using Anomaly Detection
to Identify a Minority Class
David Gerster
Vice President, Data Science
BigML

2. Traditional “Predictive Modeling”
• The famous Iris data set has measurements for 150 flowers
• Given a flower’s measurements, can we predict its species?
Iris setosa Iris versicolor Iris virginica
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3. PetalWidth(cm)
Petal Length (cm)
Iris setosa, red dots
Iris versicolor, green dots
Iris virginica, blue dots
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4. PetalWidth(cm)
Petal Length (cm)
Congratulations! You just trained a model.
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5. PetalWidth(cm)
Petal Length (cm)
PetalWidth(cm)
Petal Length (cm)
Prediction: Iris setosa
Prediction: Iris versicolor
Prediction: Iris virginica
Prediction:
Iris virginica
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6. PetalWidth(cm)
Petal Length (cm)
Prediction: Iris setosa
Prediction: Iris versicolor
Prediction: Iris virginica
Prediction:
Iris virginica
Congratulations! You just scored
four new flowers using your model,
and made a prediction about the
species of each one.
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7. PetalWidth(cm)
Petal Length (cm)
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Width <= 0.8? Width > 0.8?
Width > 1.75? Width <= 1.75?
Length <= 5? Length > 5?
50 red
45 blue
1 blue, 48 green 4 blue, 2 green
“Decision Tree”
“Leaf Nodes”
50 blue, 50 green
5 blue, 50 green
50 red, 50 blue, 50 green

8. Demo: Predictive Modeling
• Train a predictive model using the 699 biopsies
• The “label” of benign or malignant is known for each one
• Since we have labels, this is supervised learning
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9. What if we don’t have labels?
• Can we still get insight into our data if we don’t know the
colors of the dots?
• Enter anomaly detection
• Since we don’t have labels, this is unsupervised learning
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10. 10 lines are needed
to isolate this data point
(not anomalous)

11. Only 4 lines are needed
to isolate this data point
(highly anomalous)

12. Demo: Anomaly Detection
• Remove the labels of benign or malignant
• Train an anomaly detector on this unlabeled data
• Create a new dataset with the anomaly scores as “labels”
• Use these “labels” to train a predictive model!
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13. Who Needs Labels?

14. Who Needs Labels?
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15. What if we remove the malignant biopsies?
• If we remove the malignant biopsies from the dataset and do
the whole process again …
• We find a similar result!
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16. Minority Report
• This approach is well-suited for large unlabeled datasets,
especially if you expect to find an (adversarial) minority class
• Millions of credit card transactions, billions of network events …
• Doesn’t require you to know what you’re looking for!
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17. Free BigML subscription
• Use code “CERN” for a free 3-mo. BigML Pro subscription
• Handles datasets up to 4GB

18. The original “Isolation Forest” paper
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19. Q and A
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David Gerster
VP Data Science, BigML
gerster@bigml.com