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 1551–1600 of 1706 papers

TitleStatusHype
Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks—0
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification—0
Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models—0
This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering—0
Token-Level Metaphor Detection using Neural Networks—0
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification—0
``Why Should I Trust You?'': Explaining the Predictions of Any ClassifierCode0
INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words—0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement—0
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization—0
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets—0
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
Fast and Accurate Performance Analysis of LTE Radio Access Networks—0
Neural Recovery Machine for Chinese Dropped Pronoun—0
The hunvec framework for NN-CRF-based sequential taggingCode0
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction—0
Multi-Task Cross-Lingual Sequence Tagging from Scratch—0
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFCode0
Guided Cost Learning: Deep Inverse Optimal Control via Policy OptimizationCode0
Attention-Based Convolutional Neural Network for Machine Comprehension—0
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural NetworksCode0
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
Automating Feature Engineering—0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
Cognito: Automated Feature Engineering for Supervised Learning—0
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
Comparing Word Representations for Implicit Discourse Relation Classification—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Sentence Modeling with Gated Recursive Neural Network—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks—0
Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition—0
Machine Translation Evaluation using Recurrent Neural NetworksCode0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified