Cohort consensus approach to manufacturing watson Q and A pipeline training cases from historical data

摘要

A system rapidly produces training cases for machine based learning by automatically creating training cases from a database of historical data. The system determines a plurality of attributes relevant to each of the training cases. The system identifies a first attribute of the plurality of attributes as an issue, and a second attribute of the plurality attributes as a response to the issue. The system identifies a plurality of cohort members from the database of historical data, where each cohort member comprises cohort member attributes that match a subset of the plurality of attributes. The system analyzes the cohort member attributes of each of the plurality of cohort members to identify the most frequent responses to the issue. The system creates the training cases where each training case comprises the issue and the most frequent responses. The system then trains a machine based learning system using the training cases.