International Business Machines Corporation

Russell Gregory Lambert

Eugene Irving Kelton

Willie Robert Patten, Jr

Brandon Harris

Yi-Hui Ma

Shuyan Lu

Eliza Salkeld

Shanna Hayes

In an approach for managing an open feature library, a processor collects feature metadata from a data source, the feature metadata being data related to features for training a machine learning model. A processor builds a feature pipeline for the features based on the collected feature metadata. The feature pipeline may be a set of defined and active features. A processor executes the feature pipeline including computing the features. A processor executes a machine learning model using the computed features. A processor evaluates the features and corresponding model results.

Open feature library management

Computer vision and deep learning techniques are leveraged to detect behavior patterns in transaction histories. A transaction timeline is built for a series of transactions, e.g., financial, and a graphic image is constructed representing the transaction timeline. The graphic image is then matched to a known behavior pattern using a cognitive system. The cognitive system is trained with historical timeline images having associated labels. In one example the graphic image is a bar chart and each financial transaction is represented as a bar in the bar chart having a height proportional to a transaction amount, the bar being located along a time axis of the bar chart according to the transaction date and being color coded according to the transaction type.

Classifying behavior through system-generated timelines and deep learning

Growing community of inventors

Wake Forest, NC, United States of America

Russell Gregory Lambert