Optimizes classifiers for non-decomposable metrics like F-measure and Jaccard.
problem Optimizing classifiers for non-decomposable metrics in binary classification.
method Analyzes expected out-of-sample utility for F-measure and Jaccard coefficient.
result Optimal classifiers are equivalent to signed thresholding of positive class probability.
Two proofs show the triangle inequality for Jaccard distance.
problem Triangle inequality for Jaccard distance
method Simple proofs using nonnegative, monotone, submodular functions
result Triangle inequality proven for Jaccard distance
Two private algorithms estimate Jaccard similarity efficiently.
problem Estimating Jaccard similarity while preserving user privacy.
method Extends MinHash with Generalized Randomized Response and Laplace Mechanism.
result Achieves privacy-utility trade-off with theoretical bounds and experiments.
Study uses U-Net for efficient urban planning map semantic segmentation.
problem Manual visual interpretation of urban planning maps is inefficient and time-consuming.
method Utilized U-shape fully convolutional architecture for end-to-end semantic segmentation.
result Achieved high Jaccard similarity coefficient of 93.63% and overall accuracy of 99.36%.
ProbMinHash improves Jaccard similarity hashing for big data applications.
problem Efficiently estimating set similarities in big data with weighted elements.
method Locality-sensitive hash algorithms that calculate signatures collectively.
result Significantly faster than the original approach, with improved estimation error.
New algorithms optimize metrics for binary classification with class imbalance.
problem Optimizing metrics like Fβ, AM, Jaccard for imbalanced classes.
method Reformulates metric optimization as cost-sensitive learning, using surrogate loss functions.
result METRO algorithms provide strong theoretical guarantees and outperform baselines.
FUJI scores similarity of ranked lists more robustly.
problem Improving similarity assessment of ranked lists.
method Integrates a membership function into Jaccard index for better rank consideration.
result More stable and accurate similarity estimates.
New metrics improve performance in imbalanced classification problems.
problem Established metrics favor classifiers ignoring minority classes.
method Introduce robust modifications of F-score and MCC.
result TPR is bounded away from 0 in imbalanced settings.
The paper proposes methods to directly optimize complex classification metrics.
problem Handling class-imbalanced cases with non-decomposable metrics.
method Calibrated surrogate maximization of linear-fractional utility.
result Calibrated surrogate maximization can coincide with true utility maximization under certain conditions.
Paper proposes a method to minimize non-differentiable loss functions.
problem Minimizing non-differentiable and non-decomposable loss functions.
method Learn smooth relaxations of true losses through surrogate neural networks, then optimize jointly with the prediction model.
result Empirical results show the efficiency of learning surrogate losses.
C-MinHash reduces the number of permutations needed for MinHash from thousands to just two.
problem Approximating Jaccard similarity in large binary datasets using many permutations.
method Initial permutation followed by circulant shifting of a second permutation to generate hashes.
result C-MinHash achieves unbiased Jaccard similarity estimation with uniformly smaller variance.
Efficient methods for sparse random projections improve classification accuracy in very high-dimensional data.
problem Handling very high-dimensional sparse data efficiently.
method Non-iterative and iterative classification methods using sparse random projections and Jaccard kernel.
result Non-iterative methods yield larger, more accurate models than iterative methods.
Reduced computational complexity for indoor positioning.
problem Efficiently positioning users in large areas with limited data.
method Segmentation, Jaccard index, LASSO feature selection, Bayesian MAP estimation.
result Positioning accuracy with a subset of features and sub-regions is equivalent to full data, but computation time is reduced.
Study uses multimodal machine learning to predict ICD-10 codes.
problem Improving accuracy and interpretability of ICD-10 code predictions.
method Developed separate models for text and tabular data, integrated using an ensemble method.
result Best-performing model achieved micro-F1 of 0.7633 and micro-AUC of 0.9541.
A probabilistic framework optimizes quantum clustering parameters.
problem Optimizing length parameters for quantum clustering sensitivity.
method Bayesian optimization of control parameters within a probabilistic framework.
result Optimized clustering yields better concordance with known data structure.
JORC-UMAP improves UMAP by incorporating geometric and topological priors.
problem UMAP's local Euclidean distance assumption fails to capture intrinsic manifold geometry, leading to topological tearing and structural collapse.
method JORC-UMAP introduces Ollivier-Ricci curvature as a geometric prior and Jaccard similarity as a topological prior to reinforce edges and reduce redundant links.
result JORC-UMAP reduces tearing and collapse more effectively than standard UMAP and other DR methods, as measured by SVM accuracy and triplet preservation scores.
Conformal prediction fails under severe feature turnover in COVID-19 supply chain tasks.
problem Dealing with distribution shift in conformal prediction models.
method Using COVID-19 as a natural experiment across 8 supply chain tasks, analyzing SHAP explanations.
result Coverage drops vary widely (0% to 86.7%) and correlate with single-feature dependence.
STRAPSim measures ETF portfolio similarity better than existing methods.
problem Measuring portfolio similarity for ETFs and portfolios.
method Semantic, two-level, residual-aware portfolio similarity computation.
result STRAPSim outperforms existing methods in predictive accuracy and ranking alignment.
Generative models' evaluation scores can be misleading, leading to inflated grades.
problem Misleading evaluation scores for generative models.
method Analyzed and compared various scores for evaluating synthetic vs. ground-truth data.
result The Eden score avoids grade inflation and better aligns with human perception.
This study examined how the correlation and network structure of 30 global indices and 145 local Korean indices belonging to the KOSPI 200 have changed during the 13-year period, 2000-2012. The correlations among the indices were calculated. The results showed that although the average correlations of the global indice…
IoU is not submodular, impacting machine learning applications.
problem IoU's submodularity assumption in machine learning is incorrect.
method Demonstrated through counterexamples and analysis of function properties.
result IoU is not a submodular function, invalidating its use in certain applications.
New metrics unify and generalize popular distances for data mining.
problem Unified and generalized distances for data mining.
method Introducing new metrics on sets, vectors, and functions.
result New metrics outperform traditional ones in real-valued and structured data.
Framework for consistent binary classification with complex metrics.
problem Consistent binary classification for non-decomposable metrics like F-measure and Jaccard.
method General framework for batch and online learning, applies to linear and non-linear models. Uses thresholding and normalized gradient ascent for threshold estimation.
result Simple normalized gradient ascent updates for threshold estimation, with finite-sample regret analysis.
New bias found in cluster validity indices when ground truth distribution changes.
problem Bias in external cluster validity indices when ground truth distribution changes.
method Identified new type of bias (GT bias) and studied its empirical and theoretical implications.
result GT bias can change the bias status of cluster validity indices.
TS-NMF improves topic models by incorporating user-provided labels.
problem Lack of interpretability in unsupervised topic models.
method Semi-supervised non-negative matrix factorization (TS-NMF) with user-provided labeled examples.
result TS-NMF achieves higher Jaccard similarity scores than unsupervised methods at low supervision rates.
C-OPH improves One Permutation Hashing by using a shorter circulant permutation.
problem Improving the accuracy of One Permutation Hashing (OPH) for Jaccard similarity estimation.
method Develops a new densification method using a shorter circulant permutation.
result Achieves the smallest estimation variance for Jaccard similarity.
Enhanced ensemble clustering via fast propagation of cluster-wise similarities.
problem Challenges in exploring higher-level granularity and multi-scale indirect relationships in ensemble clustering.
method A novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks.
result Proposes a new cluster-wise similarity matrix and consensus functions to achieve enhanced co-association and consensus clustering.
Machine learning predicts movie genres from summaries with high accuracy.
problem Predicting movie genres from plot summaries.
method Used Naive Bayes, Word2Vec+XGBoost, Recurrent Neural Networks, and Gated Recurrent Units (GRU) for text classification and multi-label problem.
result GRU neural networks achieve the best result with a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
Proposes a new network to improve nuclei segmentation in histopathology images.
problem Challenges in separating overlapped nuclei in histopathology images.
method Introduces a bending loss regularized network to minimize contour points with large curvatures.
result Outperforms six state-of-the-art approaches on five quantitative metrics.
Extends Tanimoto kernel to real-valued functions.
problem Measuring similarity between real-valued functions.
method Unified representation of real-valued functions via sets, derived general form of the kernel, explicit feature representation, and smooth approximation.
result General Tanimoto kernel for real-valued functions.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
Estimates set overlap and similarity using random samples.
problem Estimating set overlap and similarity with limited data.
method Binomial model for predicting set overlap, comparing to previous methods.
result Binomial model provides better estimates with small sample sizes.
New method improves reliability of LDA topic modeling by assessing stability across replicated runs.
problem LDA's reproducibility issues due to initial values and Gibbs sampling.
method Cluster replicated LDA runs using modified Jaccard coefficient and pruning algorithm.
result New measure S-CLOP quantifies LDA topic stability, improving reproducibility.
New STH distance finds patterns in event timeseries without resampling.
problem Lack of efficient analysis methods for event and state timeseries.
method Define STE-ts, propose STH, leveraging both time and state duration.
result Improved precision and computation time compared to resampled metrics.
Paper interprets EMD for sets and proposes EMI, showing EMD's inferiority.
problem Definiteness issues in EMD for set comparisons.
method Set-theoretic interpretation of EMD, proposing EMI, analyzing definiteness, and comparing EMD to EMI.
result EMD is inferior to EMI in computer vision tasks.
CNN automates vitiligo lesion segmentation quickly and accurately.
problem Manual segmentation of vitiligo lesions is time-consuming and inconsistent.
method U-Net architecture with modified contracting path, followed by watershed algorithm refinement.
result CNN achieves 73.6% Jaccard Index, significantly outperforming state-of-the-art methods.
Confidence intervals improve evaluation of binary prediction rules in data mining.
problem Uncertainty in performance measures estimation from finite datasets.
method Asymptotic normal approximations for confidence intervals, with a blurring correction.
result Improved finite sample coverage probabilities and general performance measures inference.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…
ReliefE ranks features faster and better in high-dimensional data.
problem Feature ranking in high-dimensional spaces.
method Adapting Relief algorithms to manifold embeddings.
result ReliefE outperforms traditional Relief algorithms in feature ranking.
Formula connects linking coefficients to Kontsevich integral coefficients.
problem Linking coefficients from Kontsevich integral.
method Purely combinatorial approach.
result Expresses linking coefficients as combinations of Kontsevich integral coefficients.
Computed distortion coefficients for the α-Grushin plane.
problem Analyzing the distortion coefficients of the α-Grushin plane.
method Using generalised trigonometric functions and synthetic curvature conditions.
result Estimates for distortion coefficients and a curvature condition conjecture.
Machine learning predicts Kronecker coefficients with high accuracy.
problem Predicting Kronecker coefficients from tensor products of symmetric group representations.
method Training machine learning models (NN, CNN, GBDT) to classify Kronecker coefficients as zero or non-zero.
result Trained models achieve high accuracy (≈0.98) in classifying Kronecker coefficients. New methods for faster ranking and link prediction using higher-order motifs.
problem Real-time ranking and link prediction in applications like web search.
method Higher-order ranking and link prediction methods based on closing higher-order network motifs.
result The methods are faster and more efficient than existing methods based on closing triangles.
Abstract: Determines thermoelastic coefficients from boundary data.
problem Determining coefficients of thermoelastic system from boundary information.
method Explicit expression for thermoelastic Dirichlet-to-Neumann map with variable coefficients.
result Thermoelastic Dirichlet-to-Neumann map uniquely determines coefficients on the manifold.
New filling functions for groups with coefficients show different asymptotic behavior.
problem Difficulty in filling loops with surfaces in Cayley graphs.
method Defining homological filling functions with coefficients and proving their differences.
result Filling functions for n-cycles with coefficients in different groups have distinct asymptotic behavior. Develops algorithms for optimizing multi-label metrics with provable guarantees.
problem Optimizing complex multi-label metrics like F-measure and Jaccard index.
method Principled learning algorithms based on H-consistency for generalized metrics.
result Provable H-consistency bounds for multi-label metric optimization. Abstract: Determines Lamé coefficients from boundary measurements.
problem Determining Lamé coefficients from elastic boundary measurements.
method Explicit symbol of elastic Dirichlet-to-Neumann map, partial derivatives determination.
result Elastic Dirichlet-to-Neumann map uniquely determines Lamé coefficients.
Improved 3D LiDAR data classification using product coefficients.
problem Enhancing accuracy in 3D LiDAR data classification.
method Introducing product coefficients derived from measure theory as additional features in the classification process, alongside PCA.
result Significant improvement in classification accuracy with product coefficients.