Method creates resilient classifiers by focusing on highly predictive features.
problem Adversarial attacks exploit weakly correlated features learned during training.
method Resilient feature engineering to prioritize highly predictive features.
result Serial and Parallel Composition Resilience theorems support the design of resilient classifiers.
Paper evaluates ML's resilience in detecting ransomware.
problem Resilience of ML algorithms for ransomware detection.
method Used GAN to generate dynamic features to test ML models.
result ML models degrade in performance when faced with GAN-generated samples.
Study examines equity in post-Snow Uri recovery, finds disparities.
problem Disproportionate impacts on vulnerable populations during recovery.
method County and census tract level data analysis, satellite imagery, statistical procedures.
result Negative associations between non-Hispanic whites and outages, positive associations with certain demographic variables.
FeatureEnVi aids in feature engineering with visual analytics.
problem Insufficient support for feature engineering in visual analytics tools.
method Stepwise selection and semi-automatic extraction approaches.
result Extracts heavily engineered features evaluated by multiple metrics.
HarDNN detects and protects CNNs from hardware errors.
problem Hardware errors in CNNs can corrupt inference output.
method Statistical error injection and heuristic vulnerability assessment.
result HarDNN improves CNN resilience with minimal additional computation.
Python library automates feature engineering and selection for linear models.
problem Difficulties in training and explaining complex machine learning models.
method Automated feature engineering and selection for linear models.
result Improves prediction accuracy of linear models while retaining interpretability.
Automates feature extraction for IMU-based activity recognition.
problem Manual feature engineering is time-consuming and limits IMU-based applications.
method FRESH algorithm for automated feature extraction.
result Workflow automates feature extraction from synchronized IMU sensors.
Automated feature engineering improves interpretable models without manual work.
problem Lack of interpretability in complex models causes trust and stability issues.
method Use elastic black-box models to create simpler, interpretable glass-box models.
result Extracted features from complex models improve linear model performance.
SAFE automates feature engineering for industrial tasks efficiently and scalably.
problem Efficiency and scalability of automatic feature engineering methods for industrial tasks.
method SAFE (Scalable Automatic Feature Engineering) method, which provides excellent efficiency and scalability.
result SAFE method provides prominent efficiency and competitive effectiveness in industrial tasks.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
problem Expanding and reducing feature space in tabular learning becomes computationally expensive with increased dimensionality.
method SCOPE-FE controls the search space by regulating operator and feature-pair spaces, using OperatorProbing and FeatureClustering.
result SCOPE-FE reduces feature engineering time while maintaining competitive predictive performance.
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
The authors seek financial datasets to benchmark feature engineering methods on US market data.
problem Improving predictive models for financial data science competitions.
method Feature engineering methods applied to multivariate time-series data from the US market.
result Predictive power of models tested against Numerai-Signals targets.
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 error for a given target. However, there is no well-defined basis for performing effective feature engin…
This paper shows how cyber-attacks can undermine predictive maintenance systems.
problem Cyber-attacks on IoT sensors and DL algorithms in predictive maintenance systems.
method Used LSTM, GRU, and CNN for RUL prediction; modeled false data injection attacks; evaluated impact on accuracy and resilience.
result False data injection attacks can severely impact RUL prediction, but GRU-based models are more resilient.
Graph Neural Networks improve machine learning on relational databases.
problem Training machine learning models on relational databases requires costly data extraction and feature engineering.
method Uses Graph Neural Networks to extract features from relational databases.
result Outperforms state-of-the-art automatic feature engineering methods.
Paper introduces a new index to measure financial and workplace resilience of firms.
problem Corporate resilience and its types in turbulent markets.
method Quantitative analysis of earnings expectations and implied discount rates.
result Evidence of workplace resilience amplification by financial status in the COVID-19 era.
FLFE improves machine learning by efficiently and securely transforming features.
problem Efficiently and securely transforming features in a multi-party setting.
method FLFE uses a pre-learning pattern to selectively transform features, reducing communication overhead.
result FLFE outperforms evaluation-based approaches in feature transformation efficiency.
One of the most defining features of the global financial network is its inherent complex and intertwined structure. From the perspective of systemic risk it is important to understand the influence of this network structure on default contagion. Using sparse random graphs to model the financial network, asymptotic met…
VEST automates feature engineering for time series forecasting.
problem Challenges in time series forecasting with improved performance.
method VEST combines auto-regression with statistical summarization of recent past dynamics.
result VEST significantly improves forecasting performance.
Measures financial resilience using BSDEs and their properties.
problem Measuring financial resilience in dynamic risk environments.
method Developed stochastic calculus for BSDEs with jumps, revealing resilience rate as expectation of generator.
result Resilience rate can be represented as expectation of BSDE generator, revealing properties of dynamic risk measures.
MOBONS optimizes complex systems with multi-objective Bayesian optimization.
problem Balancing multiple objectives in networked systems with varying model types.
method Leverages network representations and Bayesian optimization for grey-box systems.
result Efficiently optimizes general function networks with cyclic dependencies.
Study uses machine learning to predict potato clones suitable for processing.
problem Efficiently identifying high-yield, disease-resistant potato varieties.
method Leveraged machine learning algorithms on Russet potato clones data from Oregon.
result Non-linear models like SVM and HGBC outperform traditional linear models in agricultural trials.
Study solves utility maximization in a transient price impact market.
problem Utility maximization in a market with transient price impact.
method Developed a discrete-time model and removed market depth and resilience process restrictions.
result Solved the utility maximization problem without convexity of attainable portfolio values.
The paper analyzes how machine learning models perform under covariate shift, especially when the feature shift in x is larger than that in y.
problem Performance of machine learning models under covariate shift with heterogeneous feature changes.
method Empirical risk minimization (ERM) over functions f+g, fit on a training distribution, evaluated on a test distribution with covariate shift. result ERM is more resilient to heterogeneous covariate shifts when the class F is simpler than G. A new framework for generating predictive features in noisy multivariate time series.
problem Predicting noisy multivariate time series with limited user effort.
method Develops a feature programming framework based on spin-gas dynamical Ising models.
result Validated the method on synthetic and real-world datasets.
Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessa…
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
A shallow Bi-LSTM model forecasts Bitcoin prices using engineered features.
problem Lack of high-quality historical Bitcoin data for deep learning models.
method Proposed a shallow Bi-LSTM model with feature engineering on Bitcoin data.
result A shallow deep neural network outperforms other models in Bitcoin price forecasting.
ABM automates feature engineering and variable selection for loss-based models.
problem Improving model performance through better feature engineering and variable selection.
method ABM uses group and fused lasso regularization to automatically select cutting points and variables.
result ABM integrates feature engineering, variable selection, and model training.
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
Study proposes a new resilience metric for stock market performance analysis.
problem Quantifying resilience cycles in stock market performance.
method Systems-oriented approach with Robustness Range and Elasticity Threshold.
result New metric quantifies non-linear resilience cycles in stock markets.
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
problem Understanding how much a Rademacher chaos can withstand adversarial sign-flips without significant probability changes.
method Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree.
result Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree, especially meaningful for constant degree.
Framework for renewable energy forecasting and feature engineering.
problem Forecasting and feature extraction for multivariate processes in renewable energy.
method Derivative-free optimization, ensemble of sequence-to-sequence networks, additive resampling, Bootstrap aggregating.
result The proposed method outperforms other machine learning techniques in long-term forecasts and feature selection.
Enhances machine learning for dynamic, interconnected entities.
problem Lack of systematic feature engineering for dynamic, interconnected entities.
method Augments current graph machine learning with comprehensive feature engineering in space and time.
result Improves supervised learning on heterogeneous, attributed entities interacting over time.
Paper explores understanding of neural source code embeddings.
problem Lack of understanding of contents and characteristics of code2vec embeddings.
method Small case study using code2vec embeddings to create binary SVM classifiers and compare performance with handcrafted features.
result Code2vec embeddings perform similarly to handcrafted features and have more evenly distributed information gains.
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
problem Accurate prediction of all-cause in-hospital mortality in healthcare.
method Comprehensive feature engineering approach using vital signs, laboratory results, and demographic data.
result Random Forest model achieved highest performance with AUC of 0.94, significantly outperforming other models.
New model shows negative resilience can improve trading efficiency.
problem Optimal trade execution in limit order books with negative resilience.
method Stochastic order book model with negative resilience.
result Negative resilience can lead to more efficient trading.
In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach…
Proposes resilience metrics for large blackout costs with logarithmic resilience.
problem Large variations in blackout costs make estimating risk impractical.
method Uses mean of log of large blackout costs, tail slope index, and frequency.
result Solves problems of heavy tail and large variations in blackout costs.
Feature engineering plays an important role in the success of a machine learning model. Most of the effort in training a model goes into data preparation and choosing the right representation. In this paper, we propose a robust feature engineering method, Randomized Union of Locally Linear Subspaces (RULLS). We generat…
Automates feature extraction from JSON data for machine learning.
problem Manual feature engineering for JSON data is laborious, lossy, and prone to bias.
method Automates feature extraction using Mill.jl and JsonGrinder.jl.
result Creates a differentiable machine learning model from raw JSON samples.
Study improves mortality prediction in ICU patients using feature engineering and 1D CNN.
problem Improving mortality prediction in ICU patients with high-dimensional, imbalanced, and missing data.
method Feature engineering, 1D Convolutional Neural Network (1D CNN), traditional machine learning algorithms.
result Best AUC of 0.848 achieved with 1D CNN model.
Study shows topological features improve time series classification.
problem Classifying stochastic processes with varying noise and sampling.
method Topological data analysis features compared to statistical and raw features.
result Topological features lead to better classification performance.
Study proposes using auxiliary classification to improve unsupervised anomaly detection.
problem Challenging anomaly detection in high-dimensional data.
method Use of an auxiliary classification task to extract features from unlabelled data by supervised learning.
result Our feature learning approach yields best anomaly detection performance.
Simple feature engineering beats complex models in financial prediction.
problem Understanding when complex models outperform simple alternatives in financial prediction.
method Independent Component Analysis (ICA), Wavelet Coherence, Long Short-Term Memory (LSTM) networks with attention mechanisms.
result A simple linear model using normalized flows achieves superior returns compared to complex models.
The paper investigates how irrelevant features affect clustering performance.
problem The challenge of identifying relevant features in unsupervised clustering tasks.
method Investigation of clustering performance with added irrelevant features.
result Different types of irrelevant features impact clustering outcomes differently.
Polish hate speech detection team second in PolEval 2019.
problem Low-resource hate speech detection in Polish.
method Fine-tuning ULMFiT and BERT models, automated feature engineering with TPOT.
result TPOT's shallow model achieved second place in PolEval 2019.
A new method speeds up distributed learning while maintaining robustness.
problem Efficient and robust distributed machine learning in high dimensions.
method Multi-Bulyan for gradient aggregation in distributed learning.
result Multi-Bulyan achieves Byzantine resilience and near-averaging speed with linear complexity in d.