Cognito: Automated Feature Engineering for Supervised Learning

By Khurana Udayan, T. Deepak, S. Horst, P. Srinivasan. 2016

148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “More recently, greedy search (Dor and Reich, 2012; Khurana et al., 2016) and depth-first search (Lam et al.”
Survey on Automated Machine Learning (2019, background): “A simple approach is a greedy ranking based on the candidate parents performance (Khurana et al., 2016).”
Techniques for Automated Machine Learning (2019, methodology): “Edges are SIGKDD Explorations Volume 22, Issue 2 37 Category Technique Bayesian Optimization Reinforcement Learning Evolutionary Algorithm Gradient Approach Otherss Automated Feature Engineering (AutoFE) - FeatureRL [49] NFS [14] Genetic Programming Feature Engineering [84] Genetic Programming Skewe”
One button machine for automating feature engineering in relational databases (2017, unknown): “Cognito [7] is another system that automates feature engineering but from a single database table.”
Feature Engineering for Predictive Modeling using Reinforcement Learning (2017, methodology): “We report the accuracy of (a) base dataset; (b) Our FE routine with RL1, Bmax = 100; (c) Expansion-reduction implementation where all transformations are first applied separately and add to original columns, followed by a feature selection routine; (d) Random: randomly applying a transformation func”
Learning Feature Engineering for Classification (2017, methodology): “Others perform greedy feature construction and selection based on model evaluation [Dor and Reich, 2012; Khurana et al., 2016].”