Papers Automated Feature Engineering
“Automated Feature Engineering” 태그가 달린 논문 46편 · 필터 해제
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecti…
Automated Feature EngineeringBIG-bench Machine LearningFeature EngineeringMeta-Learning+1Machine Learning for Detecting Data Exfiltration: A Review
Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration…
Automated Feature EngineeringBIG-bench Machine LearningFeature EngineeringSystematic Literature ReviewDIFER: Differentiable Automated Feature Engineering
Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) t…
Automated Feature EngineeringBIG-bench Machine LearningDecoderFeature EngineeringCardea: An Open Automated Machine Learning Framework for Electronic Health Records
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, fo…
Automated Feature EngineeringAutoMLBIG-bench Machine LearningFeature Engineering+1A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely st…
Automated Feature EngineeringFeature EngineeringSystematic Literature ReviewBenchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification
In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it pro-vides a publicly available benchmark dataset manually tagged against 6 classes. S…
Automated Feature EngineeringBIG-bench Machine LearningClassificationDeep Learning+7Lifting Interpretability-Performance Trade-off via Automated Feature Engineering
Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, achieving satisfactory a…
Automated Feature EngineeringFeature EngineeringStatistical and machine learning ensemble modelling to forecast sea surface temperature
In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches w…
Automated Feature EngineeringBIG-bench Machine LearningFeature EngineeringWeather ForecastingTowards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactio…
Automated Feature EngineeringFeature EngineeringFraud DetectionMissing ValuesTechniques for Automated Machine Learning
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning proce…
Automated Feature EngineeringAutoMLBayesian OptimizationBIG-bench Machine Learning+4Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish
This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, …
Automated Feature EngineeringClassificationFeature EngineeringGeneral Classification+3The autofeat Python Library for Automated Feature Engineering and Selection
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine le…
Automated Feature EngineeringFeature EngineeringregressionIL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural languag…
Automated Feature EngineeringFeature EngineeringRepresentation LearningBenchmarking Automatic Machine Learning Frameworks
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process. A wide range of techniques is taken to address this, however there does not …
Automated Feature EngineeringAutoMLBenchmarkingBIG-bench Machine Learning+4Layered TPOT: Speeding up Tree-based Pipeline Optimization
With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeli…
Automated Feature EngineeringAutoMLBIG-bench Machine LearningHyperparameter OptimizationAutoLearn - Automated Feature Generation and Selection
In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications. For others, feature engineering require…
Automated Feature EngineeringFeature EngineeringFeature ImportanceregressionSolving the "false positives" problem in fraud prediction
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction industry. It is estimated that only 1 in 5…
Automated Feature EngineeringFeature EngineeringPredictionvalidFeature Engineering for Predictive Modeling using Reinforcement Learning
Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling erro…
Automated Feature EngineeringEfficient ExplorationFeature Engineeringreinforcement-learning+2One button machine for automating feature engineering in relational databases
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from …
Automated Feature EngineeringFeature EngineeringLearning Feature Engineering for Classification
Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space explorat…
Automated Feature EngineeringClassificationFeature Engineeringfeature selection+1