MRI image quality affects statistical and predictive analysis of brain morphology.
problem Impact of MRI image quality on statistical and predictive analysis of brain morphology.
method Systematic testing of image quality on univariate statistics and machine learning classification using three large datasets.
result Low-quality MRI data significantly affects detecting significant sex/gender differences in smaller samples, but not in larger ones.
New method boosts multiclass classification speed and accuracy.
problem Multiclass classification problem
method Factorized MultiClass Boosting (decomposing into regression tasks, solving with CART trees)
result Significantly faster and same quality as state-of-the-art solutions
EGFC learns from streaming data to classify power quality disturbances.
problem Real-time detection and classification of power quality disturbances.
method Evolving Gaussian Fuzzy Classification (EGFC) framework with semi-supervised learning.
result Encouraging classification results from online data streams.
Model uses satellite imagery to assess road quality.
problem Inefficient monitoring of road investments in developing regions.
method Convolutional neural networks on high-resolution satellite imagery.
result Models achieve 88% binary and 73% 5-category classification accuracy.
Improves model classification accuracy in black-box settings.
problem Difficulty in inferring model properties due to limited query access.
method Introduces discriminative factorization to distinguish high-quality queries.
result Probability of chance-level classification decreases exponentially with query budget.
Low-cost sensor fusion for organic substance classification.
problem Non-destructive classification and quality control of organic substances.
method Arduino Mega 2560 microcontroller with three sensors, supervised learning models, hyperparameter tuning, PCA/LDA, ensemble voting classifier.
result Test accuracies of 93-94% for identifying organic compounds.
Study compares uncertainty estimation methods for Bayesian Neural Networks.
problem Quality of uncertainty quantification in Bayesian Neural Networks.
method Empirical comparison of 10 inference methods on regression and classification tasks.
result Common inference metrics can be misleading, and methods designed to capture posterior structure do not always produce high-quality approximations.
Unsupervised deep learning detects image quality issues without annotations.
problem Anomaly detection in images with quality issues.
method Use of deep generative models, specifically normalizing flows, for anomaly detection.
result Accurate semantic labeling and quality trends observed in images.
Study evaluates quality of uncertainty estimates for neural networks.
problem Lack of principled assessment methods for evaluating uncertainty quality in deep learning.
method Statistical methods of frequentist interval coverage, interval width, and expected calibration error.
result Different UQ methods produce markedly different quality uncertainty estimates.
Study identifies pitfalls in assessing hierarchies for multi-class classification.
problem Lack of understanding in selecting hierarchies for multi-class classification.
method Analyzed and compared popular approaches to extracting hierarchies.
result Hierarchy quality becomes irrelevant when using powerful classifiers.
The paper uses thermodynamics to improve machine learning representation quality.
problem Improving the quality of learned representations for transfer learning.
method Formal connection with thermodynamics, iso-classification process, traversing the equilibrium surface.
result Demonstrates how to transfer representations while keeping classification loss constant.
A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is pro…
Study evaluates margin parameter effects on knowledge embedding quality.
problem Understanding margin parameter's impact on embedding quality.
method Examined margin parameter values for multi-relational categorized data.
result Lower margin values are insufficient, while larger values cause noise.
Efficient deep learning model classifies fractures from X-rays.
problem Manual X-ray examination of fractures is time-consuming and error-prone.
method Robust training loop using transfer learning and latest dataset.
result Model achieves superior performance in less than ten epochs.
In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approa…
Paper compares two multi-label classification methods: Bayes metaclassifier and soft-confusion-matrix.
problem Comparing multi-label classification methods under the same framework.
method Bayes metaclassifier combined with problem-transformation approach, and soft-confusion-matrix classifier.
result Both methods perform similarly, but statistical analysis provides insights.
Enhanced nearest neighbor improves accuracy in crowdsourced data.
problem Noise in crowdsourced labels degrades classification accuracy.
method Developed two algorithms to estimate worker quality and an enhanced nearest neighbor classifier.
result Proposed methods achieve the same regret as oracle version based on expert data.
Faster SVMs trained with multilevel approach.
problem Training time inefficiency for SVMs on large datasets.
method Label propagation algorithm to construct a hierarchy of smaller SVM problems.
result Up to orders of magnitude faster than previous fastest algorithm.
Enhances GNNs by improving input data quality from topology and labels.
problem Poor quality of graph data limits GNN performance.
method Improves graph data quality using model outputs for better semi-supervised node classification.
result SEG consistently improves GNN performance across various datasets.
Unified framework detects overfitting in crash classification models.
problem Evaluation metrics fail to detect overfitting in crash classification models.
method Random Matrix Theory and Heavy-Tailed Self-Regularization framework applied to various model types.
result Power-law exponent α reliably distinguishes well-regularized from overfit models.
Enhances early-exit neural networks for anytime classification.
problem Lack of guaranteed prediction quality improvement with longer computation time.
method Post-hoc modification based on Product-of-Experts to enforce conditional monotonicity.
result Achieves conditional monotonicity in prediction quality, enabling anytime classification.
A novel 'outside the box' solution improves classification quality for imbalanced data.
problem Class imbalance in real-world data sets affects classifier performance.
method Enrichment technique using external data sets, implemented in three approaches.
result Average improvement of 27%, best case 66% in classification quality.
Node2vec embeddings are unstable and unstable with parameter choices.
problem Stability and robustness of node2vec embeddings for graph classification.
method Analysis of node2vec embeddings from multiple perspectives.
result Node2vec embeddings are unstable with respect to parameter choices.
Scores measure certainty and doubt in classification predictions.
problem Quantitative uncertainty assessment in classification problems.
method Intuitive scores in Bayesian and frequentist frameworks.
result Measures assess and compare prediction quality and uncertainty.
Paper proposes a method to protect DCNNs from adversarial attacks and poor quality images.
problem DCNNs are vulnerable to adversarial examples and perceptual quality issues.
method Combines image quality assessment with knowledge distillation.
result Detector effectively identifies poor quality and adversarial images.
BeMF improves recommendation reliability in recommender systems.
problem Improving reliability in recommender systems beyond accuracy.
method Bernoulli Matrix Factorization (BeMF) for model-based collaborative filtering.
result BeMF selects more reliable predictions, improving recommendation quality.
Paper proposes a new approach to stabilize GAN training by treating generated data as unlabeled.
problem Traditional GAN training treats generated data as negative, ignoring their potential quality.
method Defines positive and unlabeled classification for GANs, treating generated data as unlabeled.
result PUGAN achieves comparable or better performance than sophisticated discriminator stabilization methods.
In many classification problems unlabelled data is abundant and a subset can be chosen for labelling. This defines the context of active learning (AL), where methods systematically select that subset, to improve a classifier by retraining. Given a classification problem, and a classifier trained on a small number of la…
New SSIM measure improves adversarial attacks' perceptual quality.
problem Lack of perceptual quality in traditional adversarial attacks.
method Replaced Lp norms with SSIM measure for better quality. result SSIM-constrained attacks achieve better quality and similar success rate.
Data quality issues have attracted widespread attention due to the negative impacts of dirty data on data mining and machine learning results. The relationship between data quality and the accuracy of results could be applied on the selection of the appropriate algorithm with the consideration of data quality and the d…
In the recent years, we have witnessed the development of multi-label classification methods which utilize the structure of the label space in a divide and conquer approach to improve classification performance and allow large data sets to be classified efficiently. Yet most of the available data sets have been provide…
Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business, etc. In many applications, we are faced with the reality of errorfully observed graphs. That is, the existence of an edge between two vert…
Neural network improves K-factor estimation for OFDM systems.
problem Estimating Ricean K factor for link quality in OFDM systems.
method Classified as a classification problem, neural network estimates K factor at transmitter side.
result High accuracy in K factor estimation with reduced feedback bandwidth.
A new model classifies surface anomalies in 3D point cloud data.
problem Accurate classification of surface anomalies in manufacturing processes.
method Deep subspace learning approach for 3D point cloud data.
result The method effectively identifies new types of anomalies.
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity. Recent work has shown that DPPs can be effective models for product recommendati…
In this work we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification. We proposed a novel solution to the problem of correcting label pairwise ensembles. The main step of the correction procedure is to compute classifier-specific c…
New ensemble models classify mouse movement trajectories to assess survey question difficulty.
problem Assessing survey question difficulty based on respondents' interaction data.
method Ensemble models combining semi-metric-based weak learners to classify multivariate functional data.
result Improved survey data quality through better identification of respondent difficulty.
A new method for handling imbalanced big data using ensembles and smart data.
problem Imbalanced data distribution in big data scenarios.
method Smart Data driven Decision Trees Ensemble (SD_DeTE) methodology.
result SD_DeTE outperforms Random Forest in handling imbalanced binary classification problems in big data.
New research highlights flaws in evaluating clustering algorithms using classification datasets.
problem Flaws in evaluating clustering algorithms using classification datasets.
method Advanced visualization and dimension reduction techniques to expose flaws.
result Current practice of evaluating clustering algorithms may produce misleading results.
Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classificatio…
Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datasets with a number of large-domain categorical features are a difficult challenge for any classifier. Most off-th…
Survey of data augmentation methods for improving deep learning on time series data.
problem Limited labeled data in real-world time series applications.
method Review and comparison of data augmentation methods for time series.
result Empirical comparison of data augmentation methods for various time series tasks.
New measure assesses time series pre-training data quality without labels.
problem Challenges in collecting diverse pre-training datasets for time series classification.
method Contrastive-learning-based foundation model and contrastive accuracy measure.
result Contrastive accuracy correlates with model performance on downstream tasks.
New technique hides adversarial attacks in complex images, making them imperceptible.
problem Lack of interpretability in Convolutional Neural Networks (CNNs) and vulnerability to adversarial attacks.
method Developed a technique that hides adversarial attacks in regions of high complexity, making them imperceptible to human observers.
result Significant evidence of concealment of adversarial attacks in images, making them imperceptible to human observers.
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods f…
Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other …
Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis is classification, which can, for instance, improve energy load forecasting in smart grids by detecting the types of electronic devices based…
Traditional classifiers can generate high-quality images comparable to generative models.
problem Separation between classifiers and generators in neural networks.
method Optimizing input gradients to produce images, using mask-based stochastic reconstruction, progressive-resolution technique, and distance metric loss.
result Traditional classifiers can generate high-fidelity images of 256imes256 resolution on ImageNet.