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Semi-Supervised Machine Learning

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Scalarr’s Semi-Supervised Machine Learning helps to validate and interpret outputs, thus explaining the fraudulent algorithm without sacrificing large-scale learning and accuracy.

Feature selection, or choosing relevant feature subsets among thousands of potentially irrelevant and redundant features, is a very challenging task of fraud pattern recognition and large-scale machine learning. Another problem with many machine learning methods is generalization and interpretability, especially when it comes to unsupervised machine learning or neural networks. By implementing the Semi-Supervised Algorithm, Scalarr provides a supervised rule base generator, which helps to interpret and validate outputs.


Conditional anomaly detection
and predictions on unseen data

Large-scale learning without
sacrificing accuracy

Conditional anomaly detection is the task when one set of variables defines the context when the other set is investigated for the abnormal values. To get the deep image of the ever-changing fraud paradigm nature Scalarr implements the approach, thus helping to predict the results on unseen data.

Large-scale learning without sacrificing accuracy is often only realistic with the implementation of semi-supervised methods, which use both unlabeled and labeled data, thus reducing computation efforts. With a lot of noisy data onboard, the Scalarr’s Semi-Supervised Machine Learning makes fraud pattern recognition more comprehensive and acute.


Interpretability and

The biggest task Scalarr’s Semi-Supervised Machine Learning solves is the outputs interpretation and validation. By diving deep into the set of abnormalities traced and uncovered by Unsupervised Machine Learning Algorithm, it helps easily explain the results and define the exact type of detected fraud.

Decode the nature of fraud with Scalarr’s Semi-Supervised Machine Learning

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