Papers Automated Feature Engineering
“Automated Feature Engineering” 태그가 달린 논문 46편 · 필터 해제
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both c…
Automated Feature EngineeringFeature Engineeringreinforcement-learningReinforcement LearningLLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined tra…
Automated Feature EngineeringFeature Engineeringfeature selectionFederated Automated Feature Engineering
Automated feature engineering (AutoFE) is used to automatically create new features from original features to improve predictive performance without needing significant human intervention and domain expertise. Many algor…
Automated Feature EngineeringFeature EngineeringFederated LearningAdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI Researchers
The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine le…
Automated Feature EngineeringAutoMLFeature EngineeringIncremental LearningSemantic-Guided RL for Interpretable Feature Engineering
The quality of Machine Learning (ML) models strongly depends on the input data, as such generating high-quality features is often required to improve the predictive accuracy. This process is referred to as Feature Engine…
Automated Feature EngineeringDeep Reinforcement LearningFeature EngineeringKnowledge GraphsIIFE: Interaction Information Based Automated Feature Engineering
Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance. While traditional feature engineering requires significant d…
Automated Feature EngineeringFeature EngineeringOptimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
In tabular prediction tasks, tree-based models combined with automated feature engineering methods often outperform deep learning approaches that rely on learned representations. While these feature engineering technique…
Automated Feature EngineeringFeature Engineeringfeature selectionLearned Feature Importance Scores for Automated Feature Engineering
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automating this capability can relieve much manu…
Automated Feature EngineeringFeature EngineeringFeature ImportanceTime SeriesDynamic and Adaptive Feature Generation with LLM
The representation of feature space is a crucial environment where data points get vectorized and embedded for upcoming modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of…
Automated Feature EngineeringFeature EngineeringFeature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively. Recent advance…
Automated Feature EngineeringDecision MakingEfficient ExplorationFeature Engineering+2FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction
Automated Feature Engineering (AutoFE) has become an important task for any machine learning project, as it can help improve model performance and gain more information for statistical analysis. However, most current app…
Automated Feature EngineeringFeature EngineeringFeature Programming for Multivariate Time Series Prediction
We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework. This framework generates large amounts of predictive features for noisy multivariate time…
Automated Feature EngineeringFeature EngineeringInductive BiasPrediction+2Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems. We present an approach for doing so by harnessing the power of large lan…
Automated Feature EngineeringAutoMLFeature EngineeringCatch: Collaborative Feature Set Search for Automated Feature Engineering
Feature engineering often plays a crucial role in building mining systems for tabular data, which traditionally requires experienced human experts to perform. Thanks to the rapid advances in reinforcement learning, it ha…
Automated Feature EngineeringDecision MakingFeature Engineeringreinforcement-learning+1Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering
Feature engineering is widely acknowledged to be pivotal in tabular data analysis and prediction. Automated feature engineering (AutoFE) emerged to automate this process managed by experienced data scientists and enginee…
Automated Feature EngineeringFeature EngineeringNeural Architecture SearchToward Efficient Automated Feature Engineering
Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on …
Automated Feature EngineeringComputational EfficiencyFeature EngineeringFeature Selection with Distance Correlation
Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k.a. feature selection -- is an important step in solving any problem with machine learning. While there is a clear trend tow…
Automated Feature EngineeringFeature Engineeringfeature selectionfseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables us…
Automated Feature EngineeringBenchmarkingClassification with Costly FeaturesDistributed Computing+3Automated Feature Extraction on AsMap for Emotion Classification using EEG
Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learni…
Automated Feature EngineeringClassificationEEGElectroencephalogram (EEG)+3Supervised Video Summarization via Multiple Feature Sets with Parallel Attention
The assignment of importance scores to particular frames or (short) segments in a video is crucial for summarization, but also a difficult task. Previous work utilizes only one source of visual features. In this paper, w…
Automated Feature Engineeringimage-classificationMultimodal Deep LearningSupervised Video Summarization+1