International Business Machines Corporation
ONLINE MACHINE LEARNING-BASED MODEL FOR DECISION RECOMMENDATION

Abstract:

Aspects of the invention include selecting an activity as a selected activity. A method includes designating a subset of the set of activities as classes, collecting a log of inputs and outputs of each encountered activity as a data point each time the process is implemented, and extracting features from each data point that is collected to generate a feature vector from each data point. A teacher model is initialized with a first data point and updated with each data point subsequent to the first data point. A student model is initialized with a set of data points including the first data point such that every one of the classes is encountered at least once. The student model is updated with the teacher model. A set of features is input to the student model to obtain a prediction of the outcome of the selected activity.