Paper proposes a probabilistic method to handle missing data in decision trees.
problem Handling missing data in decision trees.
method At deployment time, use density estimators to compute expected predictions. At learning time, fine-tune tree parameters to minimize expected prediction loss.
result Effective compared to baselines in experiments.
Paper develops a model for handling outliers and missing data.
problem Handling outliers and missing data in data with scale mixture of normal distributions.
method Developed a scale mixture of Normal distributions model with latent variables. Inference through Variational Bayesian Approximation.
result Model effectively handles outliers and missing data.
New method improves prediction accuracy in business process mining by handling concept drift.
problem Improving prediction quality in business process mining affected by concept drift.
method Systematically analyzed and compared different data selection strategies for retraining machine learning models.
result Improved accuracy from 0.5400 to 0.7010 with concept drift handling.
Trinary decision tree improves handling of missing data in machine learning.
problem Improving accuracy in decision tree algorithms when dealing with missing data.
method Introduces Trinary decision tree, which does not assume missing values contain information about the response.
result Trinary decision tree outperforms other algorithms in Missing Completely at Random settings, especially when data is only missing out-of-sample.
Novel kernel-based PSI algorithm handles non-linearity and structured data.
problem Non-linearity and structured data in independence measures.
method Develops a PSI algorithm using HSIC, capable of handling non-linearity and structured data.
result Successfully identifies important features in real-world data.
Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.
problem Impact of missing data mechanisms and simple imputation methods on fairness of machine learning algorithms.
method Three popular datasets for classification fairness were used. Missing values were generated using three missing data mechanisms. Various missing data handling techniques (listwise deletion, mean imputation, mode imputation, multiple imputation) were applied to the datasets. Fairness was assessed using classification algorithms (random forests).
result Missing data mechanism does not significantly impact fairness; listwise deletion gives highest fairness on average.
New approach handles changing data distributions via reused models.
problem Handling concept drift in streaming data.
method Model reuse with adaptive weights based on performance.
result Adaptive model weights improve performance on various datasets.
A new decision tree algorithm handles missing values efficiently.
problem Data sets with missing values.
method Proposes a decision tree algorithm that allows user-guided data partitioning and handles missing values.
result The algorithm produces more accurate and interpretable results than common procedures without imputations.
Paper proposes HI-VAE for handling incomplete heterogeneous data.
problem Handling incomplete and heterogeneous data using VAEs.
method Proposes HI-VAE framework for fitting real-valued, positive real valued, interval, categorical, ordinal and count data.
result HI-VAE outperforms supervised models trained on incomplete data.
New method handles metric space data for regression.
problem Handling heterogeneous data like curves, images, and shapes.
method Fréchet trees and Fréchet random forests for metric space valued regression.
result Random forests can now handle complex data types.
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.
PyTorch Frame simplifies multi-modal tabular learning with modular data and model handling.
problem Handling complex multi-modal tabular data in deep learning.
method A PyTorch-based framework that provides a data structure, model abstraction, and integration with external models.
result Demonstrated the effectiveness of PyTorch Frame in implementing and applying diverse tabular models to complex multi-modal tabular data.
VAEM extends VAEs to handle mixed-type data heterogeneity.
problem Heterogeneous data with different types and marginal distributions.
method Two-stage training approach to handle mixed-type data.
result VAEM improves deep generative model performance on diverse tasks.
Study handles in 4-manifolds with cyclic fundamental group.
problem Understanding handle decompositions for specific 4-manifolds.
method Constructed handle decompositions based on fundamental group and Betti numbers.
result Handle decomposition formula for specific 4-manifolds.
A new approach switches between simple and complex models to handle concept drifts in regression tasks.
problem Handling concept drifts in regression models to maintain accurate predictions over time.
method Error Intersection Approach: switches between simple and complex models based on drift detection.
result The Error Intersection Approach significantly outperforms baselines in handling concept drifts in a real-world taxi demand dataset.
Elliptic surfaces without 1-handles proven for specific cases.
problem Proving elliptic surfaces have no 1-handles.
method Analyzing handle decompositions of elliptic surfaces E(n)p,q for specific n. result Proven existence of handle decompositions without 1-handles for specific elliptic surfaces.
A method for classification using pairwise similarities and unlabeled data.
problem Handling pairwise similarities and unlabeled data for classification.
method Empirical risk minimization approach to create an unbiased risk estimator.
result Derives an unbiased risk estimator for handling both similarities and unlabeled data.
The paper develops methods to handle missing data using regularized M-estimation in reproducing kernel Hilbert space.
problem Handling missing data in statistical analysis.
method Kernel ridge regression for imputation and maximum entropy method for propensity score estimation.
result The proposed methods achieve statistical consistency and asymptotic equivalence.
Improved online classification for manual material handling using wearable sensors.
problem Online monitoring of manual material handling activities using wearable sensors.
method Optimizes dictionary learning to improve sparse representation classification (SRC) accuracy and computational efficiency.
result Proposed method outperforms benchmark methods in accuracy and computational time for online monitoring.
Two new methods improve clustering with missing data.
problem Handling missing data in Gaussian Mixture Models.
method Proposes two methods using Monte Carlo Expectation-Maximization (MCEM) for data augmentation.
result Proposed methods outperform multiple imputation in clustering and density estimation.
Study of 3-handle attachments in 4D manifolds using Kirby calculus.
problem Inclusion of 3-handles in Kirby diagrams for 4D manifolds.
method Developed moves to extend Kirby calculus, established homological criterion.
result Identified geometric basis of 3-handle attachments, proved uniqueness theorem.
The study shows conditions for elliptic surfaces without 1-handles.
problem Finding conditions for elliptic surfaces without 1-handles.
method Analyzing knot surgeries and log-transformations of elliptic surfaces.
result Conditions for elliptic surfaces without 1-handles are established.
New deep probabilistic model handles missing data in time series forecasting.
problem Handling missing data in time series forecasting.
method Combination of deep learning and probabilistic methods.
result Advantage in forecasting and novelty detection with missing data.
Proposes a new autoencoder-based method for handling missing data.
problem Missing data bias in various domains.
method Multiple imputation using overcomplete denoising autoencoders.
result Significantly outperforms state-of-the-art methods in handling missing data.
This paper tackles imbalanced data in binary classification problems.
problem Imbalanced data leads to skewed results in classification problems.
method Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach.
result Synthetic data points enhance understanding of oversampling techniques.
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
Combines pseudo-point and state space approximations for scalable GPs.
problem Handling large numbers of off-the-grid spatial data-points and long time-series.
method Combines pseudo-point approximations for spatial data with state space GP approximations for temporal data.
result Combined approach is more scalable and applicable to a greater range of spatio-temporal problems.
New method handles missing data and multiple data types in time series models.
problem Handling missing data and multiple data modalities in time series models.
method Factorized inference method for Multimodal Deep Markov Models (MDMMs).
result Method performs well even with high levels of missing data and outperforms existing approaches.
New method reduces data complexity and handles missing values.
problem High-dimensional data with missing values.
method Random Forest imputation for missing values, statistical reduction.
result Efficiency demonstrated in numeric examples.
New algorithm detects and handles concept drift in data streams.
problem Handling concept drift in data streams for timely predictions.
method Hybrid Forest algorithm combining Hoeffding Trees and fast startup.
result The algorithm outperforms other methods in classification and regression tasks.
New method for clustering large multi-view data.
problem Handling large multi-view data efficiently.
method Incremental minimax optimization based fuzzy clustering (IminimaxFCM).
result IminimaxFCM outperforms related methods in clustering accuracy.
Develops new algorithms for QRF to handle mixed-frequency and longitudinal data.
problem Handling mixed-frequency and longitudinal data in quantile regression.
method Mixed-Frequency Quantile Regression Forest (MIDAS-QRF) and Finite Mixture Quantile Regression Forest (FM-QRF).
result Valid and flexible models for complex empirical settings in financial risk management and climate-change impact evaluation.
Harer-Kas-Kirby conjectured that every handle decomposition of the elliptic surface E(1)_{2,3} requires both 1- and 3-handles. We prove that the elliptic surface E(n)_{p,q} has a handle decomposition without 1-handles for n≥1 and (p,q)=(2,3),(2,5),(3,4),(4,5).
Study examines how network architecture handles increasing data complexity.
problem Understanding how network architecture affects performance with complex data.
method Empirical study comparing various network architectures on an image classification task with increasing class numbers.
result Modern architectures show better generalization performance with increasing data complexity.
We review Giroux's contact handles and contact handle attachments in dimension three and show that a bypass attachment consists of a pair of contact 1 and 2-handles. As an application we describe explicit contact handle decompositions of infinitely many pairwise non-isotopic overtwisted 3-spheres. We also give an alter…
Paper introduces methods to handle missing data in probabilistic regression trees.
problem Handling missing data in probabilistic regression trees.
method Three approaches: uniform probability, partial observation, and dimension-reduced smoothing.
result Preserves interpretability while extending applicability to incomplete datasets.
Topological Bayesian Optimization finds optimal structures using topological data.
problem Optimizing complex structured data like material or neural network structures.
method Extract topological information from structures using persistent homology, apply Bayesian optimization with kernels for persistence diagrams.
result Topological information improves search efficiency for optimal structures.
D.Nash defined a family of homotopy 4-spheres in [11]. Proving that his manifolds Sm,n,m′,n′ are all real S4, we find that they have handle decomposition with no 1-handles, two 2-handles and two 3-handles. The handle structures give new potential counterexamples of Property 2R conjecture.
Geometric proof shows topological invariance of handle homology.
problem Topological invariance of handle homology in manifolds.
method Entirely geometric proof using Cerf theory.
result Proof of ∂2=0 in chain complex defined by handle decomposition. New findings show infinitely many knots cannot be smoothly round handle slices.
problem Understanding the smoothability of knots in 4-dimensional space.
method Analyzing the properties of knots under surgery conjectures and cobordism conjectures.
result Infinitely many knots fail to be smoothly round handle slices.
New methods handle both data and network heterogeneity in federated learning.
problem Challenges in federated learning due to data and network heterogeneity.
method Two novel client selection schemes that minimize theoretical runtime to convergence.
result Our methods are at least competitive to and up to 20 times better than existing baselines.
Kernel ridge regression imputation with consistent variance estimation for handling missing data.
problem Handling missing data in statistical analysis.
method Kernel ridge regression imputation combined with entropy method for variance estimation.
result Root-n consistency of the imputation estimator in a Sobolev space setting.
We use the conformal method to obtain solutions of the Einstein-scalar field gravitational constraint equations. Handling scalar fields is a bit more challenging than handling matter fields such as fluids, Maxwell fields or Yang-Mills fields, because the scalar field introduces three extra terms into the Lichnerowicz e…
A new RBM model handles both linear and log-amplitude spectrograms.
problem Handling amplitude spectra with existing models.
method Proposed gamma-Bernoulli RBM that uses gamma distribution.
result The model can naturally handle positive numbers and log-amplitude spectrograms.
A 2-dimensional braid over an oriented surface-knot F is presented by a graph called a chart on a surface diagram of F. We consider 2-dimensional braids obtained by an addition of 1-handles equipped with chart loops. We introduce moves of 1-handles with chart loops, called 1-handle moves, and we investigate how muc…
Homology handles with trivial Alexander polynomial bound a 3D sphere.
problem Understanding when homology handles bound 3D spheres.
method Using Freedman and Quinn's result for Z-homology 3-spheres. result A distinguished homology handle with trivial Alexander polynomial bounds a homology S1imesD3. The paper constructs contractible manifolds with knotted spheres.
problem Creating contractible manifolds with non-standard boundaries.
method Using handles and constructing knotted spheres in SnimesS2. result Contractible (n+3)-manifolds with non-standard boundaries are constructed. This paper uses ML to identify prey handling in seals.
problem Automatically classify prey handling activity in seals for monitoring.
method Developed and compared three ML algorithms: Input Delay Neural Networks, Support Vector Machines, and Echo State Networks.
result Echo State Networks outperformed other algorithms in terms of accuracy and F1score.