SOTAVerified

Feature Engineering

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.

Papers

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Showing 1601–1650 of 1706 papers

TitleStatusHype
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
Deep Attentive Sentence Ordering Network—0
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features—0
DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection—0
Deep Exhaustive Model for Nested Named Entity Recognition—0
Deep Feature Learning for Wireless Spectrum Data—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
Deep Hashing: A Joint Approach for Image Signature Learning—0
Deep Health Care Text Classification—0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions—0
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?—0
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks—0
Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding—0
Deep Learning for Chinese Word Segmentation and POS Tagging—0
Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems—0
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities—0
Deep Learning for Iris Recognition: A Review—0
Deep Learning for NLP (without Magic)—0
Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion—0
Deep Learning in Lexical Analysis and Parsing—0
Deep Learning in Semantic Kernel Spaces—0
Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective—0
Deep Learning Regression of VLSI Plasma Etch Metrology—0
DeepLink: A Novel Link Prediction Framework based on Deep Learning—0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning—0
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction—0
Deep Neural Baselines for Computational Paralinguistics—0
Deep Neural Mobile Networking—0
Deep Neural Solver for Math Word Problems—0
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets—0
Deepr: A Convolutional Net for Medical Records—0
Deep Ranking for Person Re-identification via Joint Representation Learning—0
Deep Recurrent Neural Network for Protein Function Prediction from Sequence—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
DeepSoft: A vision for a deep model of software—0
Deep Style Match for Complementary Recommendation—0
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature—0
Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Detecting Troll Tweets in a Bilingual Corpus—0
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified