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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1676–1700 of 1706 papers

TitleStatusHype
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
Parsing with Compositional Vector Grammars—0
Learning Semantic Textual Similarity with Structural Representations—0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis—0
Additive Neural Networks for Statistical Machine Translation—0
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Investigation of annotator's behaviour using eye-tracking data—0
Recurrent Convolutional Neural Networks for Discourse Compositionality—0
Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report—0
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis—0
UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling—0
SZTE-NLP: Sentiment Detection on Twitter Messages—0
WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs—0
Sentiment Analysis of Political Tweets: Towards an Accurate Classifier—0
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent—0
Role of Morpho-Syntactic Features in Estonian Proficiency Classification—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations—0
FeatureForge: A Novel Tool for Visually Supported Feature Engineering and Corpus Revision—0
Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data—0
Joint Feature Selection in Distributed Stochastic Learning for Large-Scale Discriminative Training in SMT—0
Deep Learning for NLP (without Magic)—0
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench—0
Rep\'erage des entit\'es nomm\'ees pour l'arabe : adaptation non-supervis\'ee et combinaison de syst\`emes (Named Entity Recognition for Arabic : Unsupervised adaptation and Systems combination) [in French]—0
Show:102550
← PrevPage 68 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified