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 1576–1600 of 1706 papers

TitleStatusHype
SimpleDS: A Simple Deep Reinforcement Learning Dialogue SystemCode0
Space-Time Representation of People Based on 3D Skeletal Data: A ReviewCode0
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Automating Feature Engineering—0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty—0
Named Entity Recognition with Bidirectional LSTM-CNNsCode0
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural NetworksCode0
Performing Highly Accurate Predictions Through Convolutional Networks for Actual Telecommunication Challenges—0
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding—0
OmniGraph: Rich Representation and Graph Kernel Learning—0
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation—0
Probabilistic Bag-Of-Hyperlinks Model for Entity LinkingCode0
Machine Translation Evaluation using Recurrent Neural NetworksCode0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Multilingual discriminative lexicalized phrase structure parsing—0
Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks—0
Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition—0
Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing—0
Non-lexical neural architecture for fine-grained POS Tagging—0
Sentence Modeling with Gated Recursive Neural Network—0
ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified