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Deepak Turaga

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5 papers

1 author row

AAAI Conference 2018 System Paper

Dataset Evolver: An Interactive Feature Engineering Notebook

Fatemeh Nargesian
Udayan Khurana
Tejaswini Pedapati
Horst Samulowitz
Deepak Turaga

We present DATASET EVOLVER, an interactive Jupyter notebook-based tool to support data scientists perform feature engineering for classification tasks. It provides users with suggestions on new features to construct, based on automated feature engineering algorithms. Users can navigate the given choices in different ways, validate the impact, and selectively accept the suggestions. DATASET EVOLVER is a pluggable feature engineering framework where several exploration strategies could be added. It currently includes meta-learning based exploration and reinforcement learning based exploration. The suggested features are constructed using well-defined mathematical functions and are easily interpretable. Our system provides a mixed-initiative system of a user being assisted by an automated agent to efficiently and effectively solve the complex problem of feature engineering. It reduces the effort of a data scientist from hours to minutes.

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AAAI Conference 2018 Conference Paper

Feature Engineering for Predictive Modeling Using Reinforcement Learning

Udayan Khurana
Horst Samulowitz
Deepak Turaga

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-deﬁned basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process signiﬁcantly inﬂuences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly captures the space of given options. A highly efﬁcient exploration strategy is derived through reinforcement learning on past examples.

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IJCAI Conference 2017 Conference Paper

Learning Feature Engineering for Classification

Fatemeh Nargesian
Horst Samulowitz
Udayan Khurana
Elias B. Khalil
Deepak Turaga

Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space exploration through evaluation-guided search, or explicit expansion of datasets with all transformed features followed by feature selection. Such approaches incur high computational costs in runtime and/or memory. We present a novel technique, called Learning Feature Engineering (LFE), for automating feature engineering in classification tasks. LFE is based on learning the effectiveness of applying a transformation (e. g. , arithmetic or aggregate operators) on numerical features, from past feature engineering experiences. Given a new dataset, LFE recommends a set of useful transformations to be applied on features without relying on model evaluation or explicit feature expansion and selection. Using a collection of datasets, we train a set of neural networks, which aim at predicting the transformation that impacts classification performance positively. Our empirical results show that LFE outperforms other feature engineering approaches for an overwhelming majority (89%) of the datasets from various sources while incurring a substantially lower computational cost.

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AAAI Conference 2015 Conference Paper

Budgeted Prediction with Expert Advice

Kareem Amin
Satyen Kale
Gerald Tesauro
Deepak Turaga

We consider a budgeted variant of the problem of learning from expert advice with N experts. Each queried expert incurs a cost and there is a given budget B on the total cost of experts that can be queried in any prediction round. We provide an online learning algorithm for this setting with regret after T prediction rounds bounded by O q C B log(N)T, where C is the total cost of all experts. We complement this upper bound with a nearly matching lower bound Ω q C B T on the regret of any algorithm for this problem. We also provide experimental validation of our algorithm.

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AAAI Conference 2015 Conference Paper

Towards Cognitive Automation of Data Science

Alain Biem
Maria Butrico
Mark Feblowitz
Tim Klinger
Yuri Malitsky
Kenney Ng
Adam Perer
Chandra Reddy

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e. g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.

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