International Business Machines Corporation

Ebay, Incorporated

Udayan Khurana

Neelakantan Sundaresan

Deepak Srinivas Turaga

Nishith Parikh

Horst Cornelius Samulowitz

Gyanit Singh

Brian Scott Johnson

Mohammad Al Hasan

Srinivasan Parthasarathy

Long H Vu

Venkata Nagaraju Pavuluri

Chuang Gan

Kavitha Srinivas

Dakuo Wang

Oktie Hassanzadeh

Lisa Amini

Abel Nicolas Valente

Daniel Karl I Weidele

Elias Khalil

Arunima Chaudhary

Sainyam Galhotra

Carolina Maria Spina

Fatemeh Nargesian

A dataset including features and values associated with the features can be received. Each of the features in the dataset can be mapped to a corresponding node in a knowledge graph based on the concept represented by the corresponding node. The knowledge graph can be traversed to find a candidate node connected to at least one mapped node, the candidate node not being mapped to a feature in the dataset. A concept associated with the candidate node can be identified as a new feature. A machine learning model pipeline can use the features in the dataset and the new feature to select a subset of features for training a machine learning model.

Interactive feature engineering in automatic machine learning with domain knowledge

Embodiments relate to a system, program product, and method for employing feature engineering to improve classifier performance. A first machine learning (ML) model with a first learning program is selected. The first selected ML model is operatively associated with a first structured dataset. First features in the first dataset directed at performance of the selected ML model are identified. A second structured dataset is assessed with respect to the identified features in the first dataset, and new features in the second dataset are identified, where the new features are semantically related to the identified features in the first dataset. The first dataset is dynamically augmented with the identified new features in the second dataset. The dynamically augmented first dataset is applied to the selected ML model to subject an embedded learning algorithm of the selected ML model to training using the augmented first dataset.

Knowledge aided feature engineering

Embodiments for feature engineering by one or more processors are described. A plurality of transformations are applied to a set of features in each of a plurality of datasets. An output of each of the plurality of transformations is a score. For each of the sets of features, selecting those of the plurality of transformations for which said score is above a predetermined threshold. A signal representative of said selection is generated.

Methods and systems for feature engineering

Embodiments relate to analyzing dataset. A method of analyzing data is provided. The method obtains a description of a dataset. The method automatically generates a plurality of analysis options from the description of the dataset. The method generates a plurality of queries based on the analysis options. The method deploys the queries on the dataset to build a plurality of statistical models from the dataset.

Automatic enumeration of data analysis options and rapid analysis of statistical models

Query suggestions are provided using a query log including a number of user sessions that comprise training data. The training data includes a sequence of a plurality of sets of queries, some of the sets of queries including query transitions followed by a purchase related event. From a cleaned and normalized query log stationary scores and transition scores of at least some of the plurality of sets are generated. A set of query suggestions is built and similarity scores are computed for at least some of the set of query suggestions to determine whether individual ones of the at least some of the set of query suggestions meet a predetermined assurance level. Those that meet the assurance level are included as elements of the set of query suggestions. The set of query suggestions is mixed and ranked according to a user behavior that is sought to be influenced.

Query suggestion for e-commerce sites

Providing query suggestions using a query log including a number of user sessions that comprise training data including a sequence of a plurality of sets of queries. Some of the sets of queries include query transitions followed by a purchase related event. The query log is cleaned and normalized. Query log stationary scores and transition scores of at least some of the plurality of sets is generated. A set of query suggestions is built and similarity scores are computed for at least some of the set of query suggestions to determine whether individual ones of the at least some of the set of query suggestions meet a predetermined assurance level. Those that meet the level are included as elements of the set of query suggestions that meet the predetermined assurance level. That set of query suggestions are mixed and ranked in accordance with a user behavior sought to be optimized.

Query suggestion for E-commerce sites

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College Park, MD, United States of America

Udayan Khurana