Feature Engineering
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Benchmarks
2019_test set
Most implemented
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Wide & Deep Learning for Recommender Systems
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Deep & Cross Network for Ad Click Predictions
Named Entity Recognition with Bidirectional LSTM-CNNs
Papers
Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers
Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produc…
Feature EngineeringState of Health Estimation using Convolutional and Bidirectional LSTM Neural Networks tuned by Bayesian Optimization
In this research, a novel framework is proposed for the SOH estimation, which employs a hybrid deep learning architecture of a concatenation of a Convolution Neural Network (CNN) and a Bidirectional Long Short-Term Memor…
Feature EngineeringSymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Automated Feature Engineering (AutoFE) overc…
Feature EngineeringFuture Querying: Can LLMs Serve as Implicit Medical World Models?
Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured text. To address this, we introduce future querying, a paradigm that p…
Feature EngineeringGraph Representation Learning of Lightweight IoT Ciphers
SIMON and SIMECK belong to a family of Lightweight Cryptographic Algorithms (LCAs) based on the Feistel block cipher, designed for Internet of Things (IoT) devices. As with all Feistel ciphers, they are susceptible to di…
Graph Representation LearningFeature EngineeringThe impact of feature engineering and an optimisation framework for ocean colour machine learning
Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparameter tuning. The optimisation of the data that feeds the models - i.e.…
Feature Engineering