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 426–450 of 1706 papers

TitleStatusHype
Comparing Word Representations for Implicit Discourse Relation Classification—0
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition—0
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging—0
A Simple and Effective Approach to the Story Cloze Test—0
Complex Word Identification: Convolutional Neural Network vs. Feature Engineering—0
Computational Models for Academic Performance Estimation—0
Computing committor functions for the study of rare events using deep learning with importance sampling—0
Computing Committor Functions for the Study of Rare Events Using Deep Learning—0
Concepts for Automated Machine Learning in Smart Grid Applications—0
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization—0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
ASM Kernel: Graph Kernel using Approximate Subgraph Matching for Relation Extraction—0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects—0
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks—0
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features—0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
Credit card fraud detection using machine learning: A survey—0
Cross-Class Relevance Learning for Temporal Concept Localization—0
A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified