ExploreKit: Automatic Feature Generation and Selection

By Gilad Katz, Eui Chul Richard Shin, Dawn Song. 2016

202 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, background): “To save the rare resource of experts experience, automated machine learning (AutoML) has been proposed as a burgeoning technology to design and train neural networks automatically, at the cost of computational resources (Feurer et al., 2015; Katz et al., 2016; Bello et al., 2017; Zoph and Le, 2016; ”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Based on the metafeatures of a candidate feature the expected loss reduction after including this candidate can be predicted using a regression model (Katz et al., 2017; Nargesian et al., 2017), reinforcement learning (Khurana et al.”
Survey on Automated Machine Learning (2019, methodology): “Based on the meta-features of a candidate feature, but also meta-features of its parents, the expected loss reduction after including this candidate can be predicted using a regression model (Katz et al., 2017; Nargesian et al., 2017).”
Techniques for Automated Machine Learning (2019, methodology): “Another way is constructing a random forest (RF) model over meta-features between datasets and new generated features [15].”
Automated Machine Learning in Practice: State of the Art and Recent Results (2019, background): “Explorekit [19] not only iteratively selects the features but also generates new feature candidates to obtain the most discriminant ones.”
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, background): “sentative features based on the original ones [32], [33].”
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, methodology): “Existing works on this topic include Data Science Machine (DSM) [10], ExploreKit [9] and FeatureHub [22].”
Recursive Feature Generation for Knowledge-based Learning (2018, methodology): “Recent work by Katz et al. (2016) uses a similar approach.”
AutoLearn — Automated Feature Generation and Selection (2017, background): “state-of-the-art models namely FCTree (FCT) [13], TFC [7] and ExploreKit (EK) [14].”
Feature Engineering for Predictive Modeling using Reinforcement Learning (2017, methodology): “A related work, Ex-ploreKit (Katz et al. 2016) expands the feature space explicitly.”
Learning Feature Engineering for Classification (2017, background): “ExploreKit [Katz et al. , 2016] also generates all possible candidate features, but uses a learned ranking function to sort them.”