International Business Machines Corporation
ARTIFICIAL INTELLIGENCE MODEL GENERATION USING DATA WITH DESIRED DIAGNOSTIC CONTENT

Abstract:

A computer receives a general predictive model and training data. The computer builds a clustering feature tree model to condense the training data into data groups. The computer applies a leave-one-out evaluation method to determine an impact value for each data groups with regard to said general predictive model. The computer identifies a diagnostic category for each data group selected from a list of categories including model-harmful data, model-neutral data, and model-helping data, in accordance with said impact value. The computer removes data in groups labelled as model-harmful from the training data and builds a modified general predictive model based on data in groups labelled as model-neutral or model-helping.