We investigate the probability distribution of order imbalance calculated from the order flow data of 43 Chinese stocks traded on the Shenzhen Stock Exchange. Two definitions of order imbalance are considered based on the order number and the order size. We find that the order imbalance distributions of individual stoc…
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48 results for “Size Imbalance”
Study of linear classifiers in infinite imbalance scenarios.
problem Behavior of linear discriminant functions in extreme imbalance conditions.
method Analysis of linear classifiers under infinite imbalance, focusing on weight function properties and limit behavior.
result Limiting coefficient vectors reflect robustness or conservatism, optimizing against worst-case alternatives.
Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-label data sets are associated with a small number of training examples, much smaller compared to the size of the data set. Class imbalance poses a key challenge that plagues most multi-label learning methods. Ensemble of Cl…
The study examines how class imbalance impacts logistic regression models in low-default credit portfolios.
problem The impact of class imbalance on logistic regression models in low-default credit portfolios.
method Simulation study with controlled data-generating mechanisms to vary class imbalance and predictor-response association strength.
result Classification accuracy decreases significantly as event rate decreases, and optimal cut-off shifts with imbalance.
This study analyzes VAEs using ID and II, revealing a transition in behaviour and distinct training phases.
problem Understanding the hidden representations and training phases of VAEs.
method Analysis using Intrinsic Dimension (ID) and Information Imbalance (II).
result VAEs exhibit a transition in behaviour and distinct training phases when the bottleneck size exceeds the Intrinsic Dimension of the data.
Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption that the numbers of samples in different classes are balanced, and many SSL algorithms show lower performance for the datasets with the imbala…
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.
Study reveals optimal price prediction through volume imbalance analysis.
problem Understanding the relationship between prices and volume imbalance in high-frequency trading.
method Developed a market-making model to analyze price-imbalance connection and solve optimization problems.
result Optimal quoting of predictive imbalance is confirmed, useful for financial regulation.
Leveraging reference-only samples for two-sample testing under size asymmetry
problem Two-sample testing under size imbalance
method Adaptive aggregation of reference-dependent representations
result Strong performance with type I error control
A fast method estimates Gaussian mixture components without iterative fitting.
problem Estimating the number of components in high-dimensional Gaussian mixtures.
method Center data, compute singular values, and count above a threshold.
result The estimator consistently recovers the true number of components under mild separation condition.
CopulaSMOTE addresses class imbalance in diabetes prediction models.
problem Class imbalance in diabetes prediction models, especially with fewer confirmed cases.
method Copula-based oversampling approach that models joint dependence structure.
result CopulaSMOTE improves minority-class recovery in larger diabetes datasets.
Self-supervised learning performs better than supervised learning on imbalanced datasets.
problem The performance gap between balanced and imbalanced pre-training with self-supervised learning is smaller than with supervised learning.
method Systematic investigation of self-supervised learning under dataset imbalance, including experiments and theoretical analyses.
result Self-supervised representations are more robust to class imbalance than supervised representations.
This paper uses GANs to generate synthetic Bitcoin address data.
problem Class imbalance in Bitcoin ground-truth datasets affects supervised machine learning results.
method Generative Adversarial Networks (GANs) for synthetic data generation.
result A 'good' GAN configuration can be found to generate synthetic Bitcoin address data with high similarity to real data.
The Subtle Interplay between Square-root Impact, Order Imbalance & Volatility: A Unifying Frameworkq-fin.TR
New framework explains market volatility and metaorder impact.
problem Reconciling contradictory observations in market microstructure.
method Introducing a new theoretical framework to describe metaorders with different signs, sizes, and durations.
result Price diffusion is ensured by long memory of cross-correlations between metaorders.
Deep learning shows ETF imbalances are more informative than market imbalances.
problem Determining causality between ETF and market imbalances.
method Deep learning econometric methodology applied to stock and ETF transactions.
result ETF imbalance messages are more informative than market imbalance messages.
Improved stock price prediction model using generalized order flow imbalance.
problem Improving stock price prediction models using new order flow imbalance indicators.
method Proposed a generalized order flow imbalance construction method and applied it to CSI 500 stocks.
result Generalized Stationarized Order Flow Imbalance (log-GOFI) shows significant improvement in explaining stock price changes.
Accuracies of survival models for life expectancy prediction as well as critical-care applications are significantly compromised due to the sparsity of samples and extreme imbalance between the survival (usually, the majority) and mortality class sizes. While a recent random survival forest (RSF) model overcomes the li…
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoringcs.LG
This paper improves credit scoring models using a novel dataset distillation technique.
problem Limited scalability of pretrained models for tabular credit scoring datasets.
method Integrates class imbalance-aware dataset distillation with pretrained models.
result Improved AUC by 2.5% in financial datasets.
This paper examines biases in foundation models under long-tailed data and proposes a method to mitigate parameter imbalance.
problem The bias introduced by imbalanced training data in foundation models affects long-tailed downstream tasks.
method The paper examines parameter imbalance and data imbalance, proposing a backdoor adjustment method to mitigate parameter imbalance.
result An average performance increase of about 1.67% on each dataset.
Market makers face a trade-off between fill probability and post-fill returns, requiring contrarian strategies.
problem Navigating the trade-off between fill probability and post-fill returns in market making.
method Analysis of live trading data from Binance Bitcoin perpetual.
result A negative correlation between maker fill likelihood and post-fill returns, necessitating contrarian strategies.
Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machi…
Recent studies have shown that imbalance ratio is not the only cause of the performance loss of a classifier in imbalanced data classification. In fact, other data factors, such as small disjuncts, noises and overlapping, also play the roles in tandem with imbalance ratio, which makes the problem difficult. Thus far, t…
This chapter tackles class imbalance in datasets to promote data democracy.
problem Class imbalance in datasets leading to biased decisions and policies.
method Statistical measures and data-level methods (oversampling, undersampling, etc.) applied to a real dataset.
result Popular data-level methods improve performance in handling class imbalance.
We analyze anomaly detection class imbalance using a solvable model.
problem Class imbalance hampers anomaly detection performance.
method We use an exact solution of the teacher-student perceptron model through replica theory.
result Optimal train imbalance is often different from 50%, influenced by intrinsic imbalance and data abundance.
Theoretical and empirical taxonomy of imbalance in binary classification.
problem Class imbalance degrades binary classification performance.
method Proposed a principled framework based on three scales: imbalance coefficient, sample-dimension ratio, and intrinsic separability. Derived closed-form Bayes errors and analyzed degradation across models.
result The triplet (η, κ, Δ) provides a model-agnostic explanation of imbalance-induced deterioration.
Proposes methods to improve multi-label learning by addressing local label imbalance.
problem Local label imbalance within minority class examples degrades multi-label learning performance.
method Introduces a measure to assess local label imbalance and two sampling approaches (MLSOL, MLUL) to address it.
result Experimental results show MLSOL and MLUL improve performance on multi-label datasets.
This paper analyzes and improves active learning techniques for real-world projects.
problem Reducing labelling effort in machine learning models with real-world constraints.
method Systematic study of active learning issues, proposing techniques to address model convergence, annotation error, and dataset imbalance.
result Presentation of two techniques to speed up active learning: partial uncertainty sampling and larger query size.
Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes sin…
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance s…
Stochastic Price Dynamics in Response to Order Flow Imbalance: Evidence from CSI 300 Index Futuresq-fin.MF
Modeling price dynamics in response to order flow imbalance in Chinese futures markets.
problem Understanding price dynamics in markets with order flow imbalance.
method Modeling order flow imbalance as an Ornstein-Uhlenbeck process with memory and mean-reverting characteristics.
result Horizon-dependent heterogeneity in conventional metrics' interaction with order flow imbalance.
Deep convolutional neural networks (CNNs) can be applied to malware binary detection via image classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final laye…
Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Networkcs.LG
Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, …
New method addresses class imbalance in federated learning.
problem Class imbalance in federated learning training data.
method Proposes a monitoring scheme to infer training data composition and a new loss function, Ratio Loss, to mitigate imbalance.
result Demonstrates effectiveness in mitigating class imbalance in federated learning, outperforming previous methods.
The study analyzes trading imbalances from SEC Form 13F-HR filings to identify profitable trading opportunities.
problem Identifying profitable trading opportunities based on SEC Form 13F-HR filings.
method Statistical analysis of holdings variations and imbalances between consecutive reporting periods.
result A profitable trading strategy can be implemented by betting against the sign of imbalances with a 1-2 month time horizon.
Study compares under-bagging with other methods for imbalanced data.
problem Comparing under-bagging with other methods for imbalanced data.
method Replica analysis of under-bagging, comparing with under-sampling and simple weighting.
result Under-bagging improves performance by increasing the majority class size.
Analysis shows data imbalance slows learning curves for minority and majority classes.
problem Data imbalance impacts learning dynamics and model performance.
method Theoretical analysis of GD and SGD, including per-class normalization.
result Data imbalance causes sub-optimal learning trajectories for minority and majority classes.
Study characterizes and mitigates imbalances in neurosymbolic learning.
problem Characterizing and mitigating class-specific risks in neural classifiers.
method Theoretical analysis and practical techniques including estimating marginal gold labels and mitigating imbalances at training and testing time.
result Learning imbalances can be greatly impacted by the symbolic component σ, unlike in supervised and weakly supervised learning.
ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.
problem Detecting nuanced performance differences in classifier fairness.
method Study of ABROCA metric's statistical properties under various conditions.
result ABROCA distributions are skewed, inflating results by chance in imbalanced classes.
Many real-world classification problems are significantly class-imbalanced to detriment of the class of interest. The standard set of proper evaluation metrics is well-known but the usual assumption is that the test dataset imbalance equals the real-world imbalance. In practice, this assumption is often broken for vari…
A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques…
A novel approach reduces class imbalance in network traffic classification.
problem Severe class imbalance in network traffic leads to poor classification performance.
method Group & Reweight strategy: clusters classes, updates weights, optimizes model.
result Improves comprehensive performance in prediction and reduces class imbalance.
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very lim…
Framework tackles class imbalance and noisy labels in active learning.
problem Class imbalance and noisy labels in real-world datasets.
method Uses foundation model priors to select informative samples for active learning.
result Substantial annotation savings (over 50%) with preserved performance and robustness.
Study reveals class disparities in balanced datasets through spectral imbalance.
problem Class disparities in balanced datasets are overlooked despite model performance gaps.
method Developed a theoretical framework and studied 11 encoders to diagnose spectral imbalance.
result Identified spectral imbalance as a source of class disparities in balanced datasets.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
problem Clustering directed networks without label supervision.
method DIGRAC uses a graph neural network with a novel imbalance loss for directed flow imbalance.
result DIGRAC outperforms 10 state-of-the-art methods on directed graph clustering.
Analyzes how class imbalance and heterogeneity affect diffusion model learning dynamics.
problem Understanding how class imbalance and heterogeneity impact the learning dynamics of diffusion models.
method Developed a high-dimensional analytical framework to study class-dependent learning in score-based diffusion models.
result Class variance is the primary determinant of learning order, favoring higher-variance classes; centroid geometry plays a secondary role.
The paper introduces a new price model based on entropy that better fits high-frequency market data.
problem Understanding fair prices in high-frequency markets with bid-ask imbalance.
method A parametrized family of prices derived from the Maximum Entropy Principle, minimizing bias given volume imbalance.
result The model can generate higher kurtosis and heavy-tailed distributions compared to standard models.
Paper optimizes internal balancing of wind and hydropower to reduce intraday market volatility.
problem Reduction of intraday market volatility for power producers with wind and hydropower assets.
method Internal balancing within the same river system and sales/purchase in a pay-as-bid intraday market.
result Reduction in short-term marginal cost and risk through internal balancing.