CNNs outperform standard chemometric methods for spectral data classification.
problem Reducing the need for pre-processing steps in spectral data analysis.
method Convolutional neural networks (CNNs) compared with SVMs and PLSR for classification and regression of spectral data.
result CNNs outperform standard chemometric methods, especially for classification tasks.
A fast, robust AMP algorithm for quadratic optimization problems.
problem Implementing robust approximate-message passing algorithms for quadratic optimization problems.
method Spectral pre-processing and mild modification of AMP algorithm iterates.
result Output solution close to AMP algorithm output for perturbed inputs.
A Python package solves source duplication in single channel LVMs using spectral regularisation.
problem Source duplication in LVMs hampers their practical use in single channel applications.
method Spectral regularisation term added to address source duplication issue.
result Spectral regularisation framework enables easier investigation and utilisation of LVMs.
Spectral embedding uses eigenfunctions of the discrete Laplacian on a weighted graph to obtain coordinates for an embedding of an abstract data set into Euclidean space. We propose a new pre-processing step of first using the eigenfunctions to simulate a low-frequency wave moving over the data and using both position a…
Framework evaluates privacy cost of non-private pre-processing in DP pipelines.
problem Privacy cost of non-private data-dependent pre-processing in DP machine learning pipelines.
method Establishes upper bounds on overall privacy guarantees using Smooth DP and bounded sensitivity.
result Explicit overall privacy guarantees for various pre-processing algorithms.
Raman spectroscopy's capability to provide meaningful composition predictions is heavily reliant on a pre-processing step to remove insignificant spectral variation. This is crucial in biofluid analysis. Widespread adoption of diagnostics using Raman requires a robust model which can withstand routine spectra discrepan…
New methods correct spectral distortions using known analyte concentrations.
problem Distorted spectral shapes from absorbing and scattering contributions.
method Modified penalized baseline correction methods that incorporate known analyte concentrations.
result Improved prediction performance on near infra-red data sets.
This work proposes a new pre-processing method for supervised learning to improve fairness without sacrificing utility.
problem Improving fairness in supervised learning without compromising model performance.
method Task-tailored pre-processing approach that balances fairness and utility.
result The proposed method preserves consistent trade-offs among multiple downstream models and improves fairness in computer vision tasks.
Optimal pre-processing reduces disparate impact by minimizing total variation distance.
problem Achieving fairness in data outputs based on protected attributes.
method Using pre-processing to enforce fairness, minimizing total variation distance between pre-processed and original data distributions.
result The problem of fairness can be formulated as a linear program, efficiently solvable.
Simple AMP algorithm robust to adversarial corruption.
problem Robust approximate message passing in spiked matrix models.
method Spectral pre-processing combined with robust spectral initialization.
result AMP output is close to correct for corrupted data.
This study evaluates data pre-processing techniques for class imbalance in biomedical data.
problem Class imbalance in biomedical datasets affects model performance.
method Resampling and feature selection techniques evaluated using SVM, C4.5, LDA, and KNN classifiers.
result Feature Selection outperforms other methods in most cases, especially with SVM.
A 3-stage method enhances hyperspectral image classification accuracy.
problem Classifying detailed classes in hyperspectral images with limited labeled data.
method Uses Nested Sliding Window and PCA for spatial consistency, SVM for spectral estimation, and TV model for spatial smoothing.
result Our method outperforms state-of-the-art algorithms, especially in scenarios with small training sets.
SMOTE is one of the oversampling techniques for balancing the datasets and it is considered as a pre-processing step in learning algorithms. In this paper, four new enhanced SMOTE are proposed that include an improved version of KNN in which the attribute weights are defined by mutual information firstly and then they …
Proposes FairRR to improve fairness in machine learning models through randomized response.
problem Achieving group fairness in machine learning models.
method Formulates group fairness as optimizing a design matrix in Randomized Response, proposing FairRR.
result Demonstrates FairRR yields excellent model utility and fairness.
Spectral learning extends matrix methods to tensors for better latent variable modeling.
problem Limitations of matrix-based spectral methods in capturing non-Gaussian data.
method Extend spectral decomposition to tensor-based methods for higher-order moments.
result Tensor decomposition can identify latent effects missed by matrix methods.
fairadapt uses causal inference to mitigate algorithmic bias in data pre-processing.
problem Mitigating algorithmic bias in machine learning predictions.
method Causal graphical model and observed data to address counterfactual questions.
result The method can help eliminate discrimination and justify fair decisions.
Matrix estimation improves individual fairness without sacrificing performance.
problem Ensuring fairness in algorithmic decision-making.
method Using singular value thresholding (SVT) to preprocess data.
result SVT pre-processing improves IF guarantees and maintains performance.
In this thesis, we propose several modelling strategies to tackle evolving data in different contexts. In the framework of static clustering, we start by introducing a soft kernel spectral clustering (SKSC) algorithm, which can better deal with overlapping clusters with respect to kernel spectral clustering (KSC) and p…
New causal approach resolves fairness and accuracy trade-offs.
problem Fairness and predictive performance are often at odds.
method Causal pre-processing methods to approximate the FiND world.
result Pre-processing resolves both fairness and accuracy trade-offs.
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully …
A new method for CT using graph-based regularization.
problem Transfer calibrations between instruments without suitable transfer standards.
method Employing manifold regularization of PLS objective to enforce invariant projections in latent variable space.
result Implicit removal of inter-device variation in predictive directions.
Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend against such adversarial attack. In this work, we propose a novel methodology to defend the existing powerful attack model. We for the first time…
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets. The discovery of a number of well-known attacks such as dataset poisoning, adversar…
Optimal LDP mechanisms reduce data unfairness in classification.
problem Reducing data unfairness in classification models.
method Developed a closed-form optimal mechanism for binary attributes and a tractable framework for multi-valued attributes.
result Optimal LDP mechanisms improve fairness in classification while maintaining accuracy close to non-private models.
Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling discrimination, limiting distor…
Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer battery life, and natural noise from the low cost sensor degrade the quality of target output which hinders adoption of deep learning on an edge d…
This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is…
Develops methods for fair classification under linear disparity constraints.
problem Disparate impacts of machine learning algorithms on protected groups.
method Bayes-optimal fair classification methods via pre-, in-, and post-processing.
result Explicit forms of Bayes-optimal fair classifiers under linear disparity measures.
Random Fourier features improve tabular deep learning convergence.
problem Tabular deep learning convergence issues.
method Random Fourier projections as a pre-processing step, projecting inputs into a fixed feature space.
result Random Fourier pre-processing accelerates tabular deep learning convergence.
Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nomi…
The paper proposes a method to ensure fairness in machine learning models.
problem Ensuring fairness in machine learning models powered by supervised learning.
method Optimal affine transport and Wasserstein-2 barycenter to characterize the Pareto frontier between prediction error and statistical disparity.
result The proposed method effectively balances prediction accuracy and fairness, as demonstrated by numerical simulations.
Improved CNN model accuracy and generalizability through data pre-processing.
problem Enhancing accuracy and generalizability of CNN-based LULC classification.
method Trials of different data preparation methods, including patch selection, size, and augmentations.
result Combining multiple grids and rotations of patches improved model accuracy and generalizability.
Improves model fairness under changing bias between labels and sensitive groups.
problem Fairness of models deteriorates when bias between labels and sensitive groups changes.
method Introduces correlation shifts to explicitly capture bias changes and proposes a pre-processing step to adjust data ratios.
result Our approach effectively improves model accuracy and fairness, both synthetic and real datasets.
FairWASP optimizes training data to reduce disparities across subgroups.
problem Reducing disparities in model outputs across different subgroups in machine learning.
method A novel pre-processing approach that minimizes Wasserstein distance to the original dataset while satisfying demographic parity.
result Integer weights are optimal, allowing FairWASP to be understood as duplicating or eliminating samples.
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by usi…
PROD method improves high-dimensional regression by handling strong correlations.
problem Violation of Irrepresentable Condition in LASSO for high-dimensional data.
method PROD procedure based on orthogonal decomposition of design matrix.
result PROD enhances performance of high-dimensional penalized regression.
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuni…
Proposes fair mapping to prevent bias in model predictions without distorting data.
problem Reduces bias in model predictions without altering the data distribution.
method Uses Wasserstein GAN and AttGAN frameworks to transform data distributions while preserving privacy and interpretability.
result Preserves data interpretability and fairness in subsequent analysis tasks.
Python tool creates machine-learning-ready solar dataset.
problem Creating a usable dataset for space weather forecasting.
method Python tool generates dataset from SoHO and SDO images, applying pre-processing.
result Dataset is machine-learning ready, free of missing data, and temporally synced.
Boosting improves data fitting while maintaining fairness guarantees.
problem Ensuring fairness in data preprocessing.
method Boosting algorithm to learn sufficient statistics of exponential families.
result The learned distribution maintains fairness guarantees while fitting the data better.
RECol generates error columns to improve outlier detection.
problem Outlier detection in data with complex relationships.
method Generates reconstruction error columns for leave-one-out feature sets.
result Improves ROC-AUC and PR-AUC values of common outlier detection methods.
New AI method improves anomaly detection across different IIoT sensors.
problem Poor performance of anomaly detection models when applied to different machines.
method Robust AI method using pre-processing and multiple models on different pumps.
result Models perform well across different environments and types of pumps.
Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the current theoretical analysis for non-convex algorithms relies heavily on the assumption that every entry is observed with exactly the same pro…
A new clustering method handles uncertain covariates efficiently.
problem Clustering with uncertain covariates in datasets.
method Greedy and optimistic clustering algorithm using non-linear transformation and empirical uncertainty sets.
result Improved performance in finding sibling stars.
NDP improves GNN efficiency by coarsening graphs without losing structure.
problem Efficiently summarize graph data for deep learning models.
method Node Decimation Pooling (NDP) reduces graph density while preserving topology.
result NDP achieves comparable performance to state-of-the-art pooling methods but with improved efficiency.
Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statisti…
Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee f…
New PCA method for derivatives problems.
problem Reducing dimensionality in derivatives pricing models.
method Supervised Principal Component Analysis (PCA)
result Improved accuracy in machine learning applications.