The paper shows how variable discretization and cost-sensitive logistic regression improve credit scoring models on imbalanced data.
problem Bias in classification models on imbalanced datasets.
method Variable discretization and cost-sensitive logistic regression.
result Improves model performance on imbalanced credit scoring data and other domains.
Develops a tool to identify abnormal blood smear results based on CBC tests.
problem Manual review of blood smears by technologists is time-consuming and inconsistent.
method Cost-sensitive Lasso-penalized additive logistic regression combined with stability selection.
result The tool correctly identifies true cutoff values for abnormal smear results.
Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be…
This study detects fake and automated accounts on Instagram.
problem Fake engagement on Instagram leads to financial loss and wrong audience targeting.
method Two datasets were created and machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines, Neural Networks, and cost-sensitive genetic algorithm were applied.
result 86% accuracy for automated accounts and 96% for fake accounts were achieved.
A new method for decision-focused learning reduces computational cost.
problem Efficiently solving combinatorial problems with uncertain parameters.
method Reframed as cost-sensitive multi-output regression, with novel loss components.
result Comparable downstream task quality with reduced computational cost.
The paper presents anomaly detection in time series data using InfluxDB and Python.
problem Anomalous data points in time series data affect decision making in water and environmental systems.
method Data cleaning, cost-sensitive machine learning (Logistic Regression, Random Forest, SVM), feature selection, and InfluxDB integration.
result Random Forest outperformed other models in detecting anomalies.
The paper explores fair regression and classification under demographic parity constraints.
problem Ensuring fairness in regression and classification models under demographic parity constraints.
method Characterizes the optimal fair regression function using a barycenter problem with optimal transport costs and studies the connection between fair classification and regression.
result The optimal fair regression function is derived from the solution to a barycenter problem with optimal transport costs, and the optimal fair cost-sensitive classifiers can be derived by applying thresholds to this function.
Paper finds a lower bound for estimating low-rank matrices in logistic regression.
problem Estimating low-rank coefficient matrices in logistic regression.
method Derives a minimax lower bound on the risk.
result The bound depends on matrix dimensions, rank, and sample size.
We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data t…
Novel bounds for logistic regression coreset construction and feature selection.
problem Efficiently summarize and reduce logistic regression inputs.
method Feature space sketching for logistic regression.
result Tight bounds for coreset construction and feature selection.
Unified framework for sparse logistic regression with nonconvex regularization.
problem Sparse logistic regression with nonconvex regularization.
method Unified framework, line search criteria for nonconvex terms.
result Effective classification and feature selection at lower computational cost.
Improves logistic regression performance on imbalanced data.
problem Imbalanced data leads to all labels being estimated as majority class.
method Uses F-measure optimization to estimate relative density ratio and approximate relative F-measure.
result Proposed method improves logistic regression performance on imbalanced data.
Prevalidated ridge regression simplifies logistic regression for high-dimensional data.
problem Efficient probabilistic classification in high-dimensional data with logistic regression.
method Developed a prevalidated ridge regression model that matches logistic regression's performance but is more computationally efficient.
result Prevalidated ridge regression achieves similar classification error and log-loss to logistic regression for high-dimensional data.
Improved sketching for logistic and ℓ1 regression with near-linear dimensions.
problem Efficiently approximate ℓ1 and logistic regression problems. method New sketching techniques achieving near-linear dimensions for both problems.
result Achieved near-linear sketching dimensions for ℓ1 and logistic regression. Paper introduces MLRH, a probabilistic model for multilabel classification.
problem Multilabel classification challenges in various domains.
method Introduces hidden variables to relax one-hot-encoding in logistic regression.
result Probabilistic model achieves competitive performance compared to other algorithms.
Safe screening rules reduce computation time in logistic regression with ℓ0−ℓ2 regularization.
problem Efficiently solving logistic regression with many features and regularization.
method Screening rules based on Fenchel dual lower bounds of strong conic relaxations.
result A high percentage of features can be safely removed before solving, leading to substantial speed-up.
Improves logistic regression performance with nonconvex programming.
problem Stochastic generalized linear regression with chance constraints.
method Nonconvex programming techniques, clustering, quantile estimation.
result Over 1 to 2 percent improvement in model performance.
Paper explores connections between loss functions and consistency in binary classification and regression.
problem Consistency in binary classification and regression applications.
method Characterization of conformable loss functions and derivation of a new Huber-type loss function.
result Margin-based loss functions are equivalent to loss functions of squared standardized logistic regression residuals.
Maximum likelihood estimator performance in logistic regression analyzed.
problem Performance of maximum likelihood estimator in logistic regression.
method Sharp non-asymptotic guarantees for existence and excess logistic risk.
result Sharp guarantees for the existence and excess risk of MLE in logistic regression.
Paper introduces imprecise logistic regression for handling uncertain data.
problem Uncertainties in data prevent traditional logistic regression from being applied effectively.
method Develops imprecise logistic regression model using intervals of possible values.
result Clearly expresses epistemic uncertainty in predictions.
We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.
PIANO speeds up multinomial logistic regression solving.
problem Handling large datasets and many classes in logistic regression.
method Parallel iterative algorithm based on Majorization Minimization.
result PIANO converges to a stationary point of Multinomial and Sparse Multinomial Logistic Regression.
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper…
A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.
problem Efficiently solving logistic regression in one pass over a data stream.
method Data-oblivious sketching approach that reduces data size to poly(μdlog n) weighted points.
result Sketching reduces data size significantly and provides approximation guarantees.
A new algorithm approximates logistic regression probabilities efficiently.
problem Efficiently approximating probabilities in logistic regression for large datasets.
method Randomized sampling-based algorithm with leverage scores.
result Accurate approximations to estimated probabilities with smaller sample sizes.
This paper analyzes stability of decision trees and logistic regression.
problem Stability of decision trees and logistic regression is analyzed to understand their performance and sensitivity.
method Two stability notions (hypothesis and pointwise hypothesis stability) are derived for decision trees and logistic regression. The stability of decision trees depends on the number of leaves, while for logistic regression, it depends on the smallest eigenvalue of the Hessian matrix. Upper bounds on generalization error are constructed.
result Logistic regression is not a stable learning algorithm.
New framework for learning from imbalanced data with theoretical guarantees.
problem Class imbalance in machine learning, especially in multi-class problems.
method Theoretical framework and new margin loss function for imbalanced classification.
result Proves strong H-consistency of the proposed margin loss function. Study shows sample complexity for logistic regression with normal covariates.
problem Estimating parameters of logistic regression with normal design.
method Analyzes sample complexity in terms of dimension and inverse temperature.
result Shows two change-points in sample complexity curve based on inverse temperature.
Logistic regression gets a new, simpler uniform bound.
problem Finding a uniform bound for logistic regression's empirical risk.
method PAC-Bayes approach with second-order expansion and Rademacher-complexity bounds.
result Provides a dimension-free uniform concentration bound.
Proposes MELODIC family for simultaneous binary logistic regression.
problem Simultaneous analysis of multiple binary response variables.
method Defines models in a reduced Euclidean space using a distance rule.
result Improves predictive accuracy through model interdependence.
A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitat…
Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable…
Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. …
Study adversarial attacks on cost-sensitive classifiers.
problem Safety-critical classification problems with cost-sensitive predictions.
method Used state-of-the-art adversarially-resistant neural networks and analyzed as a two-player zero-sum game.
result Introduced a new cost-sensitive attack that performs better than targeted attacks in some cases.
Improved classifier for PU data using logistic regression.
problem Analysis of Positive Unlabeled data under SCAR assumption.
method Fitting misspecified logistic regression model to PU data.
result The classifier performs on par or better than competitors on real data sets.
Regularization improves logistic regression performance in high-dimensional settings.
problem Improving logistic regression in scenarios with many parameters and observations.
method Introducing a convex regularizer to the negative log-likelihood function to encourage desired structures.
result Explicit expressions for various performance metrics of regularized logistic regression are derived.
A neural network solves logistic regression with ℓ1 regularization efficiently.
problem Efficiently solving logistic regression with ℓ1 regularization due to non-differentiability of ℓ1 norm. method A simple projection neural network that avoids auxiliary variables and smooth approximations.
result The neural network converges to a solution of the problem with any initial value and outperforms existing methods.
Paper finds dropout noise approximation invalid for logistic regression and neural networks.
problem Invalidity of dropout noise approximation for logistic regression and neural networks.
method Derived equivalence between dropout noise injection and L2 regularisation using divergent Taylor expansion. result Approximation approach is invalid for robust constraints and general neural network topologies.
This paper proposes CSADA to make DNNs cost-sensitive.
problem Over-parameterization challenges cost-sensitive classification in DNNs.
method CSADA framework using adversarial data augmentation.
result CSADA effectively minimizes overall cost and reduces critical errors.
Improved logistic regression for multi-omics data improves prediction and variable selection.
problem Predicting binary class labels from multi-omics datasets with varying characteristics.
method Two-step penalized logistic regression with separate variable selection for each data layer.
result Our approach selects more relevant predictors and achieves comparable prediction performance.
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost…
Paper proposes a faster federated learning method for logistic regression.
problem Federated learning for logistic regression with disjoint feature sets.
method Quasi-Newton method under additively homomorphic encryption.
result Significant reduction in communication rounds with minimal additional cost.
We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformat…
Proposes an angle-based framework for multicategory cost-sensitive classification.
problem Cost-sensitive multicategory classification challenges.
method Angle-based cost-sensitive classification framework without sum-to-zero constraint.
result Proposed boosting algorithms yield competitive classification performances.
Improved multiclass logistic regression with lower computational complexity.
problem High computational complexity in existing methods for multiclass logistic regression.
method Developed a new algorithm that achieves a lower computational complexity.
result Achieved a regret of O(log(Bn)) with computational complexity O(n1.5). Efficient logistic regression for aggregated data reduces computation time.
problem Inference for logistic regression models is computationally expensive for large datasets.
method Adapted symbolic data analysis to summarise predictor variables into histograms and use composite likelihoods.
result The method achieves comparable classification rates to full data analysis but at lower computational cost.
Paper proposes a new method for estimating conditional densities using logistic regressions.
problem Estimating conditional densities for complex distributions.
method Parametric conditional density estimation via weighted logistic regressions.
result Maximum likelihood estimates can be obtained efficiently via a block-wise alternating maximization scheme and local case-control sampling.
Machine learning improves joint default assessment by capturing non-linear dependencies.
problem Capturing non-linear dependencies among covariates for accurate joint default assessment.
method Application of machine learning techniques to credit card dataset, comparing with logistic regression.
result Machine learning outperforms logistic regression in assessing portfolio riskiness.