Publication
ICDMW 2016
Conference paper

Cognito: Automated Feature Engineering for Supervised Learning

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Abstract

Feature engineering involves constructing novel features from given data with the goal of improving predictive learning performance. Feature engineering is predominantly a human-intensive and time consuming step that is central to the data science workflow. In this paper, we present a novel system called 'Cognito', that performs automatic feature engineering on a given dataset for supervised learning. The system explores various feature construction choices in a hierarchical and non-exhaustive manner, while progressively maximizing the accuracy of the model through a greedy exploration strategy. Additionally, the system allows users to specify domain or data specific choices to prioritize the exploration. Cognito is capable of handling large datasets through sampling and built-in parallelism, and integrates well with a state-of-The-Art model selection strategy. We present the design and operation of Cognito, along with experimental results on eight real datasets to demonstrate its efficacy.

Date

02 Jul 2016

Publication

ICDMW 2016

Authors

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