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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 1421–1430 of 1706 papers

TitleStatusHype
Syntax Aware LSTM Model for Chinese Semantic Role Labeling—0
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models—0
The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger—0
Neural Networks for Negation Cue Detection in Chinese—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models—0
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification—0
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
A Multi-task Approach to Predict Likability of Books—0
Sentiment Analysis of Citations Using Word2vecCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified