Systems and methods include reception of a set of data, the set of data comprising a plurality of features, building, for each of a plurality of subsets of the plurality of features, a dimension reduction model based on the subset of features and associated values of the set of data, and, for each dimension reduction model, determination of a weight associated with each of subset of features based on the dimension model, identification of a predetermined number of features associated with the highest weights, and generation, for each dimension reduction model, of a data structure comprising the predetermined number of features and the weight associated with each of the predetermined number of features. A plurality of top features are determined based on the plurality of data structures, and a supervised learning model is trained based on the plurality of top features of the set of data.