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 476–500 of 1706 papers

TitleStatusHype
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo—0
A System for Diacritizing Four Varieties of Arabic—0
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research—0
A Multi-task Approach to Predict Likability of Books—0
A Dual-Layer Semantic Role Labeling System—0
Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
A Survey on Semantics in Automated Data Science—0
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection—0
Deep Exhaustive Model for Nested Named Entity Recognition—0
Deep Feature Learning for Wireless Spectrum Data—0
Deep Attentive Sentence Ordering Network—0
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
A Survey on Extraction of Causal Relations from Natural Language Text—0
Deep Health Care Text Classification—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data—0
Decoding and interpreting cortical signals with a compact convolutional neural network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified