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Feature Engineering

145 papers with code · Methodology

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

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Latest papers without code

Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods

29 Jul 2020

The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques.

FEATURE ENGINEERING FRAUD DETECTION

Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate

26 Jul 2020

Learning sophisticated models to understand and predict user behavior is essential for maximizing the CTR in recommendation systems.

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING RECOMMENDATION SYSTEMS

Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks

23 Jul 2020

We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language.

FEATURE ENGINEERING REPRESENTATION LEARNING

Supervised learning on heterogeneous, attributed entities interacting over time

22 Jul 2020

Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment.

FEATURE ENGINEERING

Towards Ground Truth Explainability on Tabular Data

20 Jul 2020

In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.

FEATURE ENGINEERING FEATURE SELECTION

ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level

14 Jul 2020

We evaluate ADSAGE on authentication, email traffic and web browsing logs from the CERT insider threat datasets, as well as on real-world authentication events.

ANOMALY DETECTION FEATURE ENGINEERING

Product age based demand forecast model for fashion retail

10 Jul 2020

Fashion retailers require accurate demand forecasts for the next season, almost a year in advance, for demand management and supply chain planning purposes.

FEATURE ENGINEERING

CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

5 Jul 2020

To address these issues, in this paper, we propose an approach to extract a very small number of aggregated features that are easy to interpret and compute, and empirically show that we obtain high prediction accuracy even with a significantly reduced feature-space.

FEATURE ENGINEERING

A Simple and Effective Dependency Parser for Telugu

ACL 2020

We present a simple and effective dependency parser for Telugu, a morphologically rich, free word order language.

FEATURE ENGINEERING

AraDIC: Arabic Document Classification Using Image-Based Character Embeddings and Class-Balanced Loss

ACL 2020

Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering.

DOCUMENT CLASSIFICATION FEATURE ENGINEERING MORPHOLOGICAL ANALYSIS