Abstract: A system and method for data classification. A method includes sampling a dataset into at least one first sample and at least one second sample having different data types; applying a truth table to the at least one first sample in order to obtain a first set of outputs, wherein the truth table includes a plurality of input variables and a plurality of corresponding logical operation results; applying a classifier to features extracted from the at least one second sample in order to obtain a second set of outputs, wherein the classifier is configured to classify the at least one second sample with respect to a plurality of patterns in the at least one second sample; determining at least one classification for the dataset based on the first set of outputs and the second set of outputs.

Abstract: Techniques for data classification. A method includes sampling a dataset into first and second samples. Each first sample is a numerical value, and each second sample is a string of characters. A truth table is applied to the first samples from a dataset. The truth table includes multiple first columns, each of which accepts an input value determined for each of the first samples, and a second column which outputs first scores representing likelihoods for respective classifications. Classifiers are applied to features extracted from the second samples, where each classifier is a machine learning model trained to output a second score representing a likelihood for a respective classification for each second sample. Classifications are determined based on the first and second scores. The classifications include a classification for each first sample determined based on the first scores and a classification for each second sample determined based on the second scores.

Abstract: Techniques for data classification. A method includes sampling a dataset into first and second samples. Each first sample is a numerical value, and each second sample is a string of characters. A truth table is applied to the first samples from a dataset. The truth table includes multiple first columns, each of which accepts an input value determined for each of the first samples, and a second column which outputs first scores representing likelihoods for respective classifications. Classifiers are applied to features extracted from the second samples, where each classifier is a machine learning model trained to output a second score representing a likelihood for a respective classification for each second sample. Classifications are determined based on the first and second scores. The classifications include a classification for each first sample determined based on the first scores and a classification for each second sample determined based on the second scores.