Deep SMOTE improves SMOTE's stability and accuracy in imbalanced classification.
problem Stability and accuracy issues in SMOTE for imbalanced classification.
method Adapting SMOTE idea in a deep neural network regression model.
result Deep SMOTE outperforms traditional SMOTE in precision, F1 score, and AUC.
Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose D…
The paper tackles class imbalance in deep learning models and proposes a method to enhance feature extraction.
problem Class imbalance affects deep learning models, especially in imbalanced settings.
method The paper introduces an extension of deep over-sampling to use automatically-generated abstract-labels for weak-supervision.
result The proposed framework significantly improves image classification benchmarks with imbalanced classes.
Study shows over-sampling biases prediction results on imbalanced datasets.
problem Over-optimistic prediction results on imbalanced data.
method Applying over-sampling before partitioning training and testing sets.
result Over-sampling causes biased results and reduces predictive performance.
Paper reviews and compares methods for handling imbalanced data.
problem Handling imbalanced data sets in financial industry.
method Reviewed and compared under-sampling/over-sampling methodologies.
result Performance analysis of class-imbalance methods, modeling algorithms, and grid search criteria.
Deep kernel learning for clustering improves on spectral methods.
problem Discovering effective kernels for clustering.
method Neural network producing embeddings motivated by spectral clustering, optimized via gradient adaptations on the Stiefel manifold.
result Trained embeddings outperform state-of-the-art deep clustering methods and traditional approaches.
OS-ELM outperforms other classifiers in software defect prediction.
problem Predicting software defects from imbalanced datasets.
method OS-ELM, oversampling with noise filtering, comparison with other classifiers.
result OS-ELM outperforms other classifiers in recall and balanced accuracy.
A new graph-based sampling method improves classification of imbalanced COVID-19 datasets.
problem Classification of imbalanced COVID-19 datasets.
method Modified Random Walk Under-sampling based on graphs.
result The method increases the boundary of the minority class without raising outliers.
Paper proposes an SVM-based framework to predict conductor galloping with high accuracy.
problem Predicting conductor galloping from imbalanced datasets.
method Employed smart sampling techniques (over-sampling) to balance the dataset and improve SVM performance.
result SVM-based framework achieved an F_1-score of 98.9% with only three features.
Adaptive scheduling improves multilingual neural machine translation models.
problem Training models on multiple tasks with uniform or proportional sampling leads to poor performance trade-offs.
method Exploring non-adaptive and adaptive task scheduling, including implicit schedules.
result Adaptive schedules improve model performance for low-resource tasks without negatively affecting high-resource tasks.
DRIO improves time series imputation by minimizing reconstruction error and distributional divergence.
problem Bias in imputation due to mismatch between observed and true data distributions.
method DRIO minimizes reconstruction error and worst-case divergence using Wasserstein ambiguity set.
result DRIO consistently provides robust imputation and improved forecasting.
The paper classifies cervical cancer using various techniques and feature selection.
problem Classifying cervical cancer from a dataset with missing values and imbalance.
method Feature selection, over-sampling, under-sampling, dimensionality reduction, and classification techniques.
result Age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy.
Deep learning detects traffic accidents in real time using spatiotemporal data.
problem Detecting traffic accidents to improve safety and reduce delays.
method Used LSTM and GRUs on spatiotemporal sequential data with SMOTE oversampling.
result GRU model performs slightly better than LSTM in detecting traffic accidents.
Sample variance decay is shown in deep ReLU networks, impacting training dynamics.
problem Sample variance decay in deep ReLU networks during training.
method Decomposed total variance into sample variance and network-averaged sum of sample mean and variance.
result Sample variance decays in later layers of deep ReLU networks, impacting training dynamics.
Class imbalance problems manifest in domains such as financial fraud detection or network intrusion analysis, where the prevalence of one class is much higher than another. Typically, practitioners are more interested in predicting the minority class than the majority class as the minority class may carry a higher misc…
EviTrack improves sequential prediction in delayed disambiguation scenarios.
problem Challenges in sequential prediction with delayed disambiguation where early observations are ambiguous.
method EviTrack operates over latent trajectories, applying evidence- and likelihood-ratio-based selection to delay commitment until supported by data.
result EviTrack outperforms sampling-based baselines in a controlled synthetic benchmark, achieving faster post-disambiguation recovery.
k-Nearest Neighbors is one of the most fundamental but effective classification models. In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vecto…
The paper analyzes generalization in deep contrastive learning.
problem Generalization analysis for unsupervised deep contrastive representation learning.
method Parameter-counting and norm-based bounds derived for neural networks of varying sizes and depths.
result Bounds are independent of network depth and size, reducing dependency on matrix norms.
CDP improves RL performance and sample-efficiency by prioritizing rare goal states.
problem Learning from imbalanced data in RL.
method Curiosity-Driven Prioritization (CDP) framework.
result CDP improves both performance and sample-efficiency of RL agents.
Paper uses virtual big data to improve autoencoder training and address imbalanced data classification.
problem Imbalanced data classification and autoencoder over-fitting.
method Cross-concatenation using Virtual Big Data.
result Cross-concatenation method effectively balances imbalanced class distributions.
A new method VOS uses variational autoencoders to balance imbalanced datasets.
problem Class imbalance in datasets leads to skewed predictions and false positives.
method Inspired by variational inference and GANs, VOS creates synthetic minority class samples.
result VOS outperforms traditional oversampling methods in augmenting imbalanced datasets.
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. Howev…
Deep learning models can have low bias and variance, contrary to classical theory.
problem Understanding the performance of deep learning models at high complexity.
method Developed a fine-grained bias-variance decomposition for random feature kernel regression, analyzing the effects of sampling, initialization, and labels.
result The variance terms exhibit non-monotonic behavior and can diverge at the interpolation boundary, even in the absence of label noise.
Bayesian network framework assesses urban risks across multiple domains.
problem Complex interdependencies in urban systems.
method Bayesian Belief Networks (BBNs) with DAGs, Hill-Climbing search, BIC, K2 scoring, synthetic data, SMOTE.
result Identifies key risk factors and quantifies likelihood of cascading failures.
The paper analyzes logistic regression for rare events data, deriving new insights on estimator efficiency and sampling strategies.
problem Binary logistic regression for rare events data with significantly fewer events than controls.
method Derives asymptotic distribution of MLE, proves under-sampling advantage, and compares over-sampling efficiency.
result Under-sampling a small proportion of nonevents can improve efficiency in rare events data analysis.
This study improves detection of insider threats using machine learning.
problem Detecting insider threats with anticipation and accuracy.
method Empirically tested 88 machine learning algorithms on CERT dataset, focusing on employee sentiment.
result Random forest algorithms offer the best overall performance, with high accuracy and interpretable rules.
Study uses online data to identify malicious websites, addressing imbalance.
problem Identifying malicious websites from many more benign ones.
method Integrated resampling approach combining SMOTE and PSO.
result Proposed approach outperforms other resampling methods.
New method improves Bayesian inference for large models.
problem Bayesian inference in large-scale models like neural networks.
method Non-parametric variational approximation with a modified Langevin algorithm.
result Improves convergence speed and accuracy compared to existing methods.
We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed to change with time. We introduce an algorithm whose regret with respect to any policy in a comparison class grows as t…
Research aims to predict fallen angel bonds' bankruptcy using machine learning.
problem Predicting which fallen angel bonds will become investment grade or go bankrupt.
method Used four classification methods (logistic regression, KNN, SVM, NN) and Google Cloud's automated machine learning.
result Google Cloud's machine learning model performed best in over-sampled and feature selection data sets.
The study predicts solar flare productivity using magnetic data from SDO/HMI.
problem Forecasting solar flares, especially M- and X-class, to mitigate space weather effects.
method Statistical and machine learning methods applied to 563 ARs' magnetic data.
result Improved accuracy in predicting AR's Flare Index, especially for large values.
Paper proposes a new method for feature importance in model selection.
problem Feature importance in model selection.
method New tree-model explanation approach using Coefficient of Variation of feature weight.
result Novel feature explanation performs better than cross-validation in model selection.
This paper uses synthetic data to improve machine learning performance on small, imbalanced datasets.
problem Improving machine learning performance on small and imbalanced datasets.
method Generates synthetic data through convex combination and uses it in a semi-supervised learning framework with support vector machines.
result Synthetic data over-sampling supports the cluster assumption in semi-supervised learning, leading to outstanding results for small high-dimensional datasets and imbalanced learning problems.
Proposes a technique to balance imbalanced data classes.
problem Imbalanced data in classification problems.
method Synthesizes data samples near actual data for minority classes.
result Significantly more balanced and fair classification results achieved.
Random Forest variable importance is improved by class balancing techniques.
problem Class imbalance problem in machine learning.
method Proposed a variable selection algorithm using RF variable importance and its confidence interval.
result Our algorithm efficiently selects an optimal feature set, leading to improved prediction performance.
A new inference network learns undirected models from data.
problem Learning undirected models with hidden variables is hard.
method Train a single stochastic inference network from data, using a cost function over queries.
result The network can perform inference tasks like an undirected model without partition function.
We improve kernel ridge regression for skewed responses using oversampling and adaptive partitioning.
problem Kernel ridge regression struggles with skewed response variables, leading to poor estimates.
method Combines adaptive partitioning with oversampling to address skewed responses in kernel ridge regression.
result The proposed method yields estimates with smaller risk compared to classical methods under mild conditions.
SMOTE-DP enhances synthetic data privacy without sacrificing utility.
problem Balancing privacy and utility in synthetic data generation.
method Integrating SMOTE with differential privacy mechanisms.
result SMOTE-DP produces synthetic data that is both private and useful.
The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to …
Gradient matching with Gaussian processes is a promising tool for learning parameters of ordinary differential equations (ODE's). The essence of gradient matching is to model the prior over state variables as a Gaussian process which implies that the joint distribution given the ODE's and GP kernels is also Gaussian di…
Improved MCMC for rare events in hidden Markov models.
problem Slow inference and prediction for rare latent states in hidden Markov models.
method Targeted sub-sampling (TASS) over-samples rare latent states, reducing variance in gradient estimation.
result Substantial gains in predictive and inferential accuracy on real and synthetic examples.
Uncertainty-aware PCA preserves data uncertainty during dimensionality reduction.
problem Uncertainty in data affects traditional PCA methods, leading to inaccurate results.
method Generalizes PCA for multivariate probability distributions, respecting uncertainty.
result Uncertainty-aware PCA maintains data characteristics after projection.
Study evaluates three class imbalance techniques across diverse datasets.
problem Class imbalance in binary classification tasks.
method Synthetic Minority Over-sampling Technique (SMOTE), Class Weights tuning, Decision Threshold Calibration.
result Decision Threshold Calibration is the most consistently effective technique.
Variance-Calibrated Modulation (VCM) addresses the likelihood trap in LLMs by reshaping the probability distribution before truncation.
problem LLMs fall into the likelihood trap, leading to repetitive degeneration and vocabulary dullness.
method VCM reshapes the probability distribution before truncation through Contextual Searchlight and Adaptive Self-Debiasing.
result VCM mitigates the likelihood trap across open-ended generation, factual QA, and mathematical reasoning.
The paper evaluates six imbalanced data strategies across 58 datasets.
problem Mitigating imbalanced data in binary classification problems.
method Comprehensive comparative analysis of 10 under-sampling, 5 over-sampling, 2 ensemble, and 3 specialized algorithms on 58 real-life datasets.
result The effectiveness of imbalanced data strategies varies significantly depending on the performance metric used.
WOTBoost improves minority class accuracy in imbalanced datasets.
problem Imbalanced datasets lead to low accuracy in minority class classification.
method Combines weighted oversampling and boosting techniques.
result WOTBoost achieves best G mean and highest AUC score on multiple datasets.
PARIS reduces imbalanced regression datasets by pruning uninformative samples.
problem Imbalanced regression where models focus on high-frequency regions, ignoring rare but impactful events.
method PARIS uses the representer theorem to compute a closed-form representer deletion residual for iterative pruning of the training set.
result PARIS reduces training set by up to 75% while preserving or improving overall performance, outperforming other methods.
Enhances PCE surrogates using transfer learning for expensive simulations.
problem Over-sampling in PCE for expensive forward models.
method Transfer learning from similar tasks to a new task with limited training data.
result Improves scalability and accuracy of PCE surrogates.