On-device research index

arXiv research

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.

169,181 papers · 148 categories

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48 results for “Misclassification ratios”

ClassSim measures similarity between classes using misclassification ratios of trained classifiers.

problem Evaluating similarities between similar classes in real-world datasets.

method ClassSim metric based on misclassification ratios of trained DNNs.

result ClassSim provides better similarities than existing methods for image recognition.

Deep learning Similarity metric Misclassification ratios Classifiers Image recognition

2018-02-05abs ↗pdf ↗

Neyman-Pearson multiclass classification under label noise via empirical likelihoodstat.ME

Develops NPMC method for noisy labels, improving multiclass classification accuracy.

problem Asymmetric misclassification costs and label noise in multiclass classification.

method Empirical likelihood approach using exponential tilting density ratio model.

result Root n consistent and asymptotically normal estimators for clean labels and noise mechanism.

Neyman-Pearson multiclass classification label noise empirical likelihood density ratio model asymptotic normality

2026-03-23abs ↗pdf ↗

Causal Explanations of Image Misclassificationscs.LG

Study identifies and mitigates causes of image misclassifications in CNN models.

problem Improving model interpretability and accuracy in image classification.

method Trained six CNN architectures on CIFAR-10, used conditional confusion matrices and misclassification networks to identify morphological similarity and non-essential information interference as causes of misclassification. Developed a method to reduce misclassifications by erasing pixels within top 5% saliency map bounding boxes.

result Identified two causes of misclassification: morphological similarity and non-essential information interference, and developed a method to reduce misclassifications caused by the latter.

image classification CNN models misclassification interpretability saliency map

2020-06-28abs ↗pdf ↗

On support vector machines under a multiple-cost scenariostat.ML

Proposes a novel SVM model for binary classification with different misclassification costs.

problem Real-world classification problems with varying misclassification costs.

method Incorporates performance constraints in SVM formulation to seek a hyperplane with maximal margin and misclassification rates below given thresholds.

result The proposed model gives users control over misclassification rates in one class at the expense of the other.

Support Vector Machines Binary Classification Misclassification Costs Performance Constraints Maximal Margin

2023-12-22abs ↗pdf ↗

Reliable Time Prediction in the Markov Stochastic Block Modelcs.SI

New model predicts links in community-based networks robustly.

problem Link prediction in community-based networks with local clustering errors.

method Markov Stochastic Block Model (MSBM) with Hidden Markov Model (HMM) predictions.

result Misclassification error decays exponentially with relevant signal-to-noise ratio (SNR).

Markov Stochastic Block Model HMM link prediction collaborative filtering signal-to-noise ratio

2020-04-09abs ↗pdf ↗

A Data-Driven Measure of Relative Uncertainty for Misclassification Detectionstat.ML

Paper introduces a new uncertainty measure for misclassification detection.

problem Effective detection of unreliable model predictions in machine learning.

method Data-driven measure of uncertainty relative to an observer based on soft-predictions.

result Demonstrates improved misclassification detection over state-of-the-art methods.

uncertainty measure misclassification detection machine learning soft-predictions image classification

2023-06-02abs ↗pdf ↗

Implementation of an Asymmetric Adjusted Activation Function for Class Imbalance Credit Scoringcs.LG

A new activation function improves credit scoring accuracy for imbalanced datasets.

problem Imbalanced datasets in credit scoring lead to underestimation of misclassification costs.

method Introduces ASIG, an asymmetric adjusted Sigmoid function.

result ASIG-embedded classifier outperforms traditional classifiers across various imbalance ratios.

credit scoring binary classification activation function imbalanced data misclassification cost

2025-01-21abs ↗pdf ↗

Non-Convex SGD Learns Halfspaces with Adversarial Label Noisecs.LG

Non-convex SGD learns halfspaces with adversarial label noise efficiently.

problem Agnostically learning halfspaces in adversarial label noise settings.

method Non-convex SGD optimization for halfspace learning.

result Non-convex SGD achieves misclassification error close to optimal with adversarial noise.

SGD halfspaces PAC model log-concave distributions adversarial noise

2020-06-11abs ↗pdf ↗

Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspectioncs.LG

Detects misclassifications and adversarial examples using neural network logits.

problem Unable to detect misclassifications and adversarial examples in neural networks.

method Introspection using pretrained neural network logits.

result Simple 3-layer neural network trained on logits detects misclassifications competitively.

neural networks misclassifications adversarial examples logits introspection

2019-05-22abs ↗pdf ↗

One-Bit Clustering for Two Component Sub-Gaussian Mixture Modelscs.IT

One-bit clustering method for two-component sub-Gaussian mixture models

problem Clustering in sub-Gaussian mixture models

method One-bit clustering using dithered quantization

result Decaying misclassification rate with exponential signal-to-noise ratio

clustering sub-Gaussian mixture models quantization Haar matrix

2026-06-20abs ↗pdf ↗

A New Method for Classification of Datasets for Data Miningcs.LG

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discusse…

2016-12-01abs ↗pdf ↗

RaSE: Random Subspace Ensemble Classificationstat.ML

RaSE ensemble framework improves sparse classification accuracy.

problem Sparse classification challenges in high-dimensional data.

method Random Subspace Ensemble (RaSE) framework with subspace selection via RIC.

result RaSE achieves low misclassification rates and accurate feature ranking.

ensemble classification sparse classification random subspaces subspace selection RIC

2020-06-16abs ↗pdf ↗

Robust Information Selection for Hypothesis Testing with Misclassification Penaltiesstat.ML

A new framework selects information sources to test hypotheses robustly, even with misclassifications.

problem Robust hypothesis testing with misclassification penalties.

method Introduces a misclassification penalty framework and an efficient greedy algorithm.

result Proposes a submodular surrogate metric for better selection.

hypothesis testing misclassification penalties subset selection curvature properties greedy algorithm

2025-02-20abs ↗pdf ↗

Community Detection for Contextual-LSBM: Theoretical Limitations of Misclassification Rate and Efficient Algorithmsstat.ML

Efficient algorithm for CLSBM reduces misclassification rate.

problem Reducing misclassification in community detection for CLSBM.

method Spectral-based algorithm for CLSBM, with theoretical misclassification bounds.

result Upper bound on misclassification rate of efficient algorithm.

Community Detection Contextual Labeled Stochastic Block Model Misclassification Rate Spectral Method Gaussian Mixture Model

2025-01-19abs ↗pdf ↗

Classification using Ensemble Learning under Weighted Misclassification Lossstat.ML

Method finds optimal binary classification rules under weighted misclassification loss.

problem Optimal binary classification rules for resource-limited settings with cost-sensitive decisions.

method Ensemble learning to derive prediction scores and associated thresholds minimizing weighted misclassification loss.

result Jointly derived score and threshold outperforms methods that derive score first and threshold second.

binary classification ensemble learning weighted misclassification loss resource-limited settings cost-sensitive decisions

2018-12-16abs ↗pdf ↗

High Dimensional Classification through

\ell_0

-Penalized Empirical Risk Minimizationstat.ME

The paper develops a classification method using penalties on feature selection for high-dimensional data.

problem High-dimensional binary classification with many irrelevant features.

method Empirical risk minimization with l0-penalization for feature selection.

result The method achieves a sparse solution close to true sparsity with high probability and converges to low misclassification risk.

high dimensional classification empirical risk minimization feature selection l0-penalization sparse solution

2018-11-23abs ↗pdf ↗

Minimizing The Misclassification Error Rate Using a Surrogate Convex Losscs.LG

We carefully study how well minimizing convex surrogate loss functions, corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors. In particular, we show that amongst all convex surrogate losses, the hinge loss gives essentially the best possible bound, o…

2012-06-27abs ↗pdf ↗

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margincs.LG

New algorithm learns halfspaces with large margins efficiently.

problem Learning halfspaces with large margins in noisy data.

method Proper learning algorithm with tight complexity bounds.

result Nearly tight complexity characterization for

α=1.01

-approximate learning.

Learning Theory Halfspaces Margin PAC Model Complexity

2019-08-29abs ↗pdf ↗

Adaptive Cost-Sensitive Learning in Neural Networks for Misclassification Cost Problemscs.LG

New algorithm reduces misclassification costs in neural networks.

problem Reduces costs of misclassified instances in neural networks.

method Adaptive Cost-Sensitive Learning (AdaCSL) adjusts loss function to bridge class distribution mismatches.

result Deep neural networks with AdaCSL outperform other methods on cost-sensitive binary classification tasks.

Adaptive Cost-Sensitive Learning Neural Networks Misclassification Costs Deep Learning Cost-Sensitive Classification

2021-11-14abs ↗pdf ↗

Asymptotic Behavior of Adversarial Training in Binary Classificationstat.ML

Paper analyzes adversarial training's performance in binary classification.

problem Understanding the generalization performance of adversarial training.

method Derives precise theoretical predictions for adversarial training performance.

result Provides exact asymptotics for test errors of adversarial training.

Adversarial Training Binary Classification Empirical Risk Minimization Asymptotic Behavior Over-parameterization

2020-10-26abs ↗pdf ↗

On the Minimax Misclassification Ratio of Hypergraph Community Detectioncs.IT

New method detects communities in hypergraphs, achieving optimal statistical limit.

problem Community detection in hypergraphs under stochastic block model.

method Two-step algorithm: spectral clustering followed by local refinement.

result Achieves optimal statistical limit for community detection in hypergraphs.

community detection hypergraphs stochastic block model spectral clustering local refinement

2018-02-03abs ↗pdf ↗

On the rate of convergence of image classifiers based on convolutional neural networksstat.ML

Convolutional neural networks improve image classification accuracy.

problem Improving accuracy in image classification.

method Analyzing the convergence rate of misclassification risk for image classifiers.

result A rate of convergence independent of image dimension proves the effectiveness of CNNs.

convolutional neural networks image classification curse of dimensionality misclassification risk rate of convergence

2020-03-03abs ↗pdf ↗

Semiparametric Methods for Exposure Misclassification in Propensity Score-Based Time-to-Event Data Analysisstat.ME

Proposes a method to correct exposure misclassification bias in Cox models.

problem Challenges in estimating exposure-outcome associations with misclassified exposure data.

method An estimating equation method to correct for exposure misclassification-caused bias.

result Proposed method corrects bias in estimating PM2.5 level's association with lung cancer mortality.

propensity score exposure misclassification time-to-event data Cox regression asymptotic properties

2019-03-19abs ↗pdf ↗

Misclassification cost-sensitive ensemble learning: A unifying frameworkcs.LG

Unified framework for cost-sensitive ensemble learning.

problem Different misclassification costs in data.

method A unifying framework for cost-sensitive ensemble methods.

result Unified understanding and natural extensions of existing methods.

ensemble learning cost-sensitive misclassification AdaBoost Bagging

2020-07-14abs ↗pdf ↗

Submodular Information Selection for Hypothesis Testing with Misclassification Penaltiesstat.ML

Optimal subset selection for hypothesis testing with penalties.

problem Optimal subset selection of information sources for hypothesis testing with misclassification penalties.

method Proposes a misclassification penalty framework and studies two variants of subset selection problems under centralized Bayesian learning.

result Proves the submodularity of the objective and constraints of the subset selection problems and establishes performance guarantees for greedy algorithms.

submodularity hypothesis testing misclassification penalties subset selection greedy algorithms

2024-05-17abs ↗pdf ↗

A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluationstat.ML

This paper compares two clustering evaluation metrics, revealing their differences and properties.

problem Understanding the differences between misclassification error distance and adjusted Rand index.

method Population origins, data analysis examples, detailed case studies, and simulation study.

result Reveals previous misconceptions about the two metrics and their distributions.

clustering evaluation misclassification error distance adjusted Rand index data analysis simulation study

2019-07-26abs ↗pdf ↗

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentstat.ML

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the e…

2016-10-26abs ↗pdf ↗

Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Datastat.ML

The paper develops a method to accurately estimate the Bayes misclassification error rate.

problem Estimating the best achievable classifier performance without learning a Bayes-optimal classifier.

method Learning to benchmark using an ensemble of ε-ball estimators and Chebyshev approximation.

result The proposed method achieves an optimal mean squared error rate of O(N^(-1)) under a smoothness assumption.

Bayes error rate classifier performance learning to benchmark ensemble methods Chebyshev approximation

2019-09-16abs ↗pdf ↗

Agnostic Learnability of Halfspaces via Logistic Losscs.LG

New research shows logistic regression can achieve optimal error rate for agnostic learning of halfspaces.

problem Agnostic learning of homogeneous halfspaces with logistic loss.

method Constructing a well-behaved distribution and using logistic regression with additional convex optimization steps.

result Logistic regression can achieve

Ω(\sqrt{ extrm{OPT}})

misclassification risk, matching the upper bound.

Logistic Regression Agnostic Learning Halfspaces Optimization Misclassification Risk

2022-01-31abs ↗pdf ↗

Adequacy of the Gradient-Descent Method for Classifier Evasion Attackscs.CR

Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification. In this paper, we examine the adequacy of the…

2017-04-06abs ↗pdf ↗

Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testingstat.ML

Develops GLRT for defending against adversarial attacks in hypothesis testing.

problem Adversarial attacks on machine learning models causing misclassification.

method Generalized likelihood ratio test applied to composite hypothesis testing problem.

result GLRT approach yields competitive robustness-accuracy tradeoff under various attacks.

Adversarial Attacks Hypothesis Testing Generalized Likelihood Ratio Test Robustness Nuisance Parameter

2021-12-04abs ↗pdf ↗

The LogBarrier adversarial attack: making effective use of decision boundary informationcs.LG

A new adversarial attack improves model perturbation efficiency.

problem Improving adversarial attacks to better perturb images.

method LogBarrier method for solving constrained minimization problem.

result LogBarrier attack performs better on challenging images.

adversarial attacks image classification logarithmic barrier optimization perturbation

2019-03-25abs ↗pdf ↗

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Informationmath.ST

Improved community detection in heterogeneous SBM with side information.

problem Misclassification in community detection with noisy labels.

method Optimal weighted message passing and minimum energy flow.

result Optimal weighting improves misclassification rate in heterogeneous SBM.

community detection heterogeneous SBM minimum energy flow weighted message passing side information

2017-09-12abs ↗pdf ↗

Achieving Optimal Misclassification Proportion in Stochastic Block Modelmath.ST

Community detection is a fundamental statistical problem in network data analysis. Many algorithms have been proposed to tackle this problem. Most of these algorithms are not guaranteed to achieve the statistical optimality of the problem, while procedures that achieve information theoretic limits for general parameter…

2015-05-14abs ↗pdf ↗

Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classificationcs.LG

This work aims to reduce inexplicable errors in deep neural networks by obtaining class-level semantics and penalizing misclassifications.

problem Deep neural networks misclassify images, leading to inexplicable errors that can harm trust and societal impact.

method Obtain class-level semantics, propose Weighted Loss Functions (WLFs), and train classifiers with these methods.

result Trained networks have more explicable failure modes and comparable accuracy to existing methods.

Deep Neural Networks Misclassification Explainability Weighted Loss Functions Human Semantics

2020-06-26abs ↗pdf ↗

A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliersstat.ML

Robust clustering algorithm for datasets with outliers.

problem Clustering with arbitrary outliers.

method Spectral clustering with a rounding scheme on a Gaussian kernel matrix.

result Misclassification error decays exponentially with signal-to-noise ratio.

spectral clustering sub-gaussian mixture outliers denoising signal-to-noise ratio

2019-12-16abs ↗pdf ↗

Adversarial Perturbations Against Real-Time Video Classification Systemscs.LG

Paper shows adversarial attacks are possible in real-time video classification systems.

problem Adversarial perturbations in real-time video classification systems.

method Exploits GAN architectures to generate stealthy adversarial samples.

result Generative adversarial networks can cause misclassification rates of over 80% in real-time video classification.

adversarial attacks real-time video GANs temporal correlations misclassification

2018-07-02abs ↗pdf ↗

Shallow-Deep Networks: Understanding and Mitigating Network Overthinkingcs.LG

SDN mitigates overthinking in deep networks, reducing inference cost and preserving accuracy.

problem Overthinking in deep neural networks, leading to unnecessary computations and misclassifications.

method Introducing internal classifiers (Shallow-Deep Network) to SDNs, enabling early confidence-based exits.

result SDNs reduce inference cost by more than 50% and preserve accuracy, mitigating overthinking.

overthinking deep neural networks Shallow-Deep Network confidence-based early exits inference cost reduction

2018-10-16abs ↗pdf ↗

Misclassification excess risk bounds for PAC-Bayesian classification via convexified lossstat.ML

This research improves PAC-Bayesian bounds for classification tasks using convexified loss.

problem Deriving generalization bounds for classification tasks with non-convex loss functions.

method Shift focus to misclassification excess risk bounds for PAC-Bayesian classification using convex surrogate loss and leveraging PAC-Bayesian relative bounds in expectation.

result Improved PAC-Bayesian bounds for classification tasks with convex surrogate loss.

PAC-Bayesian classification convexified loss misclassification excess risk

2024-08-16abs ↗pdf ↗

To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifierscs.LG

New attacks exploit transfer learning to misclassify text models.

problem Misclassification attacks against transfer learned text classifiers.

method Novel attack algorithms using unintended features from teacher models.

result Transfer learning increases vulnerability to misclassification attacks.

transfer learning misclassification attacks text classifiers adversarial examples GloVe

2020-01-08abs ↗pdf ↗

Robust Classification of Financial Riskstat.ML

Study shows financial models are vulnerable to small data changes, but robust algorithms can protect them.

problem Financial models are vulnerable to small data changes, leading to misclassification.

method Used adversarial attacks and an original attack to explore model sensitivity, and developed a robust optimization algorithm.

result Robust optimization algorithm can build models resistant to misclassification on perturbations.

financial services machine learning adversarial attacks robust optimization misclassification

2018-11-27abs ↗pdf ↗

Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descentstat.ML

Convolutional neural networks converge quickly with gradient descent.

problem Learning efficient image classifiers with over-parameterized networks.

method Gradient descent for training over-parametrized CNNs with global average-pooling.

result Gradient descent quickly reduces the misclassification risk of CNNs.

Convolutional Neural Networks Gradient Descent Image Classification Over-Parameterization Global Average-Pooling

2024-05-13abs ↗pdf ↗

A Compressive Classification Framework for High-Dimensional Datastat.ML

A new method for high-dimensional data classification reduces misclassification errors.

problem High-dimensional data classification with limited samples.

method Compressive Regularized Discriminant Analysis (CRDA) using joint-sparsity promoting hard thresholding and regularized covariance matrix estimators.

result CRDA gives fewer misclassification errors than competitors and accurately selects features.

high-dimensional data classification feature selection sparse estimation regularized covariance

2020-05-09abs ↗pdf ↗

A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples: BOBYQAcs.LG

BOBYQA optimizes deep networks with fewer queries than other methods.

problem Generating adversarial examples with fewer queries than non-model-based methods.

method Model-based derivative-free optimisation algorithm (BOBYQA).

result BOBYQA achieves state-of-the-art results with fewer queries than other methods.

BOBYQA adversarial examples derivative-free optimization deep networks

2020-02-24abs ↗pdf ↗

Adversarially Robust Classification based on GLRTstat.ML

Paper defends machine learning models from adversarial attacks using GLRT.

problem Adversarial attacks on machine learning models leading to misclassification.

method Generalized likelihood ratio test (GLRT) for robust classification.

result GLRT yields performance competitive with minimax approach under worst-case attacks, and better trade-off under weaker attacks.

adversarial attacks machine learning robust classification GLRT minimax

2020-11-16abs ↗pdf ↗

Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysisstat.ML

SEDA improves RLDA for high-dimensional data.

problem Inconsistent performance of RLDA in high-dimensional scenarios.

method Developed a non-asymptotic approximation of misclassification rate, derived new theoretical results on eigenvectors, and proposed SEDA algorithm.

result SEDA achieves higher classification accuracy and dimensionality reduction compared to existing LDA methods.

Regularized Linear Discriminant Analysis High-dimensional Data Misclassification Rate Spectral Enhancement Eigenvectors

2025-07-22abs ↗pdf ↗

A Cost-Sensitive Deep Belief Network for Imbalanced Classificationcs.LG

Proposes ECS-DBN for cost-sensitive deep belief network in imbalanced classification.

problem Imbalanced data classification with unequal misclassification costs.

method ECS-DBN uses adaptive differential evolution to optimize misclassification costs based on training data.

result ECS-DBN consistently outperforms state-of-the-art methods on benchmark and real-world datasets.

cost-sensitive deep belief network imbalanced classification adaptive differential evolution fault diagnosis

2018-04-28abs ↗pdf ↗

Building an Interpretable Recommender via Loss-Preserving Transformationstat.ML

We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification cost…

2016-06-19abs ↗pdf ↗