Feature Engineering
390 papers with code • 1 benchmarks • 5 datasets
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.
Benchmarks
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Libraries
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Latest papers
Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification.
Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks
dayuyang1999/merger_acquisition_prediction
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M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities.
Leveraging Latents for Efficient Thermography Classification and Segmentation
In this work, we present a novel algorithm for both breast cancer classification and segmentation.
A Two Dimensional Feature Engineering Method for Relation Extraction
The results indicate that two-dimensional feature engineering can take advantage of a two-dimensional sentence representation and make full use of prior knowledge in traditional feature engineering.
Predictive Analytics of Varieties of Potatoes
We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials.
Iterative Feature Boosting for Explainable Speech Emotion Recognition
In speech emotion recognition (SER), using pre- defined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information.
Machine Learning-Based Completions Sequencing for Well Performance Optimization
Establishing accurate field development parameters to optimize long-term oil production takes time and effort due to the complexity of oil well development, and the uncertainty in estimating long-term well production.
Descriptive Kernel Convolution Network with Improved Random Walk Kernel
In this paper, we first revisit the RWK and its current usage in KCNs, revealing several shortcomings of the existing designs, and propose an improved graph kernel RWK+, by introducing color-matching random walks and deriving its efficient computation.
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression
siyulou/unsupervisedhierarchicalsymbolicregression
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Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis.
Deep Learning Applications for Intrusion Detection in Network Traffic
The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection.
