Abstract: Methods and systems are presented for generating robust computer models related to data feature usage for classification of electronic transactions are. This technology improves computer function via new machine learning techniques to more accurately classify data. A set of dominative features may be selected from candidate features using multiple feature selection algorithms, where each dominative feature in the set of dominative features is dominative over every remaining candidate feature. The multiple feature selection algorithms may include at least one univariate feature selection algorithm and at least one multivariate feature selection algorithm. The set of dominative features may be reduced to a number of representations, where each representation represents an aspect of the set of dominative features. The representations may then be used to generate the robust computer model.

Abstract: Methods and systems for generating a targeted risk analysis model by using a knowledge transfer technique to enhance a generic risk analysis model are presented herein. The knowledge transfer may be temporal-based or domain-based. A first generic risk analysis model is generated to produce an outcome based on a set of input data related to a first set of features. The first generic risk analysis model is trained using a first set of training data having first characteristics. Based on a type of knowledge transfer requested, a second set of training data having second characteristics is obtained. The first generic risk analysis model is enhanced to produce a second targeted risk analysis model by retraining the first generic risk analysis model using the second set of training data.

Abstract: Methods and systems for generating robust computer models for detecting fraudulent electronic transactions are presented herein. A set of dominative features is selected from candidate features using multiple feature selection algorithms, where each dominative feature in the set of dominative features is dominative over every remaining candidate feature. The multiple feature selection algorithms may include at least one univariate feature selection algorithm and at least one multivariate feature selection algorithm. The set of dominative features may be reduced to a number of representations, where each representation represents an aspect of the set of dominative features. The representations may then be used to generate the robust computer model.