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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 1501–1525 of 1706 papers

TitleStatusHype
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
Breast mass classification in ultrasound based on Kendall's shape manifold—0
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Building automated vandalism detection tools for Wikidata—0
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench—0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering—0
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification—0
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Capturing ``attrition intensifying'' structural traits from didactic interaction sequences of MOOC learners—0
Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships—0
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media—0
Character Feature Engineering for Japanese Word Segmentation—0
Character-level Supervision for Low-resource POS Tagging—0
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model—0
Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks—0
Chinese Zero Pronoun Resolution with Deep Memory Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified