Method for detecting anomalies in a data set

A computer-implemented method for detecting anomalies in an unlabeled data set, wherein the unlabeled data set comprises data records of input variables and at least one output variable dependent from one or more input variables, said method comprising:

• a. from the unlabeled data set, generating, by a computing device, a training data set comprising training data records and original output classes, wherein:
• i. each training data record is obtained by excluding from a corresponding data record at least the output variable, and
• ii. an original output class corresponding to a value of the output variable of the corresponding data record is associated to the training data record;
• b. applying, by the computing device, a classification method to the training data set, to generate a classification model associating a predicted output class to the training data record, wherein the predicted output class is selected from the original output classes associated to the training data records;
• and
• c. comparing, by the computing device, the original output class and the predicted output class, wherein an anomalous data record is detected in a presence of a discrepancy between the original output class and the predicted output class.