A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning uses ROC cost functions to improve virtual screening accuracy.
Geometric analysis of ROC and PR curves for binary classification.
Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The costs of false positives and false negatives vary across application domains and receiver operating characteristic (ROC) curves provide a visu…
A scalable ROC-SVM variant reduces training time for imbalanced binary classification.
Improves ROC/AUC for multi-class classification.
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
The paper tackles fairness in scoring functions for binary classification.
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discret…
New optimization method improves AUC for binary classification and changepoint detection.
Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions and of the response belonging to either class, probability distributions for the…
Modified AUC improves CNN training by considering model confidence.
CP-ROC bands improve graph classification accuracy and uncertainty quantification.
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into "color com…
Neural network models improve ROC curve evaluation of biomarkers, focusing on age's role in physical activity-mortality association.
Novel method for multiclass ROC curves using multidimensional Gini index.
New method tests independence using ROC analysis and bipartite ranking.
Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been su…
Receiver operating characteristic (ROC) analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under ROC curve (AUC) with standard cross-validation methods suffers from a large bias. The leave-pair-out (LPO) cross-validation has been shown to correct this bias. Howe…
The fuzzy ROC extends Receiver Operating Curve (ROC) visualization to the situation where some data points, falling in an indeterminacy region, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds under indeterminacy; and visual summarization of the large number of possibili…
Evaluation often aims to reduce the correctness or error characteristics of a system down to a single number, but that always involves trade-offs. Another way of dealing with this is to quote two numbers, such as Recall and Precision, or Sensitivity and Specificity. But it can also be useful to see more than this, and …
The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the …
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…
Develops a new method for statistical optimal allocation problems.
Paper estimates optimal ROC curve arc length and AUC, improving classification performance.
We unify f-divergences, Bregman divergences, surrogate loss bounds (regret bounds), proper scoring rules, matching losses, cost curves, ROC-curves and information. We do this by systematically studying integral and variational representations of these objects and in so doing identify their primitives which all are rela…
ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.
In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function, for instance to learn a global model from the local data collected by each computing unit. In this paper, we address the problem of decentralized minimization of pairw…
Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of this significant infrastructure could affect important social, environmental, and economic impacts. Furthermore, recognizing the optimized timeline for inspection of sewer pipelines are challenging …
Proposes MCC-F1 curve for better binary classification evaluation.
AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank, etc. Many learning approaches try to optimize AUC, while owing to the non-convexity and discontinuousness of AUC, almost all …
New algorithm optimizes AUC in binary classification and changepoint detection.
Paper proposes a new algorithm to minimize AUC disparities in machine learning models.
This paper visualizes uncertainty in classifier performance metrics.
Deep ROC analysis improves model selection and interpretation in medical and AI applications.
TrialGraph uses graph machine learning to improve clinical trial design and predict side effects.
The variational autoencoder (VAE) is a generative model with continuous latent variables where a pair of probabilistic encoder (bottom-up) and decoder (top-down) is jointly learned by stochastic gradient variational Bayes. We first elaborate Gaussian VAE, approximating the local covariance matrix of the decoder as an o…
Predicts accuracy of classifiers on unseen classes.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
We show how to compute the Bayes error-rate for speaker verifiers.
DCC separates marginal estimation from dependence modeling for improved classification accuracy.
We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on proba…
The paper analyzes recalibration methods for binary classifiers under distribution shift.
The paper proves concentration inequalities for two-sample rank processes and applies them to ranking performance criteria.
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related t…
The study examines how verifier imperfections impact test-time scaling techniques.
Optimizes partial AUC across various FPRs for machine learning models.
New metrics fail adversarial tests, with some more robust than others.
Proposes Isometric Graph Neural Networks to preserve graph distances.