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 1151–1200 of 1706 papers

TitleStatusHype
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches—0
TLab: Traffic Map Movie Forecasting Based on HR-NET—0
Token-Level Metaphor Detection using Neural Networks—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—0
Topological Data Analysis for Portfolio Management of Cryptocurrencies—0
Toward Efficient Automated Feature Engineering—0
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes—0
Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
Towards explainable meta-learning—0
Towards Context-Aware Neural Performance-Score Synchronisation—0
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity—0
Towards Intelligent Risk-based Customer Segmentation in Banking—0
Towards Non-Parametric Learning to Rank—0
Towards Personalized and Human-in-the-Loop Document Summarization—0
Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus—0
Transaction Fraud Detection via an Adaptive Graph Neural Network—0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems—0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Transitional Uncertainty with Layered Intermediate Predictions—0
Transition-based Dependency DAG Parsing Using Dynamic Oracles—0
Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks—0
Transparent text quality assessment with convolutional neural networks—0
Transportation Modes Classification Using Feature Engineering—0
Treatment Side Effect Prediction from Online User-generated Content—0
Trees and Forests in Nuclear Physics—0
Trinity: A No-Code AI platform for complex spatial datasets—0
TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis—0
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder—0
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification—0
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity—0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement—0
UMUTextStats: A linguistic feature extraction tool for Spanish—0
Ensemble learning for predictive uncertainty estimation with application to the correction of satellite precipitation products—0
Understanding Generative AI Content with Embedding Models—0
Understanding LLM Embeddings for Regression—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
Unified Embedding Based Personalized Retrieval in Etsy Search—0
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition—0
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis—0
UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified