Study reduces dataset size by 3 attributes to 73% classification accuracy.
problem Reducing dataset size for gender classification in TSI Life Satisfaction Survey.
method Attribute selection using Chi2 algorithm, followed by classification tests with Naive Bayes and Random Forest.
result Best classification rate achieved with 3 attributes selected by Chi2 algorithm.
A neural network tackles emotion recognition, attribution, and summarization.
problem Sparsity of emotional expressions in videos.
method Bi-stream Emotion Attribution-Classification Network (BEAC-Net) with two networks: attribution and classification.
result Superior performance on emotion attribution, recognition, and summarization tasks.
A new network-based method for high-level data classification without normalization.
problem Evaluation and classification of data with hidden attribute interactions.
method A new network-building methodology based on attribute-attribute interactions, avoiding normalization.
result Improvement in accuracy of high-level classification using betweenness centrality.
New method builds complex networks from attribute interactions without normalization.
problem Improving high-level classification algorithms by capturing hidden attribute interactions.
method Proposes a new complex network building methodology based on attribute-attribute interactions, avoiding normalization.
result Demonstrates improved performance in high-level classification techniques.
Graph Prototypical Networks improve few-shot node classification on attributed networks.
problem Few-shot node classification in attributed networks with limited labeled instances.
method Graph Prototypical Networks (GPN) using meta-learning to extract meta-knowledge and identify informative labeled instances.
result GPN achieves superior performance in few-shot node classification.
New method explains time series classification by assessing causal effects.
problem Understanding machine learning model decisions in time series classification.
method Model-agnostic causal attribution method using diffusion models.
result Causal attributions differ from associational ones, highlighting risks.
Paper proposes embedding attributes across domains using CNN for better classification.
problem Transfer learning with stable attributes across domains.
method Embed attributes in a common space using CNN, combine domain-independent and domain-specific CNN outputs.
result Effective classification model with minimized errors.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
HyperBERT enhances BERT for node classification on text-attributed hypergraphs.
problem Challenges in capturing hypergraph structure and text attributes in node classification.
method Mixing hypergraph-aware layers with BERT for improved node classification.
result HyperBERT achieves state-of-the-art results on text-attributed hypergraph benchmarks.
ASTRID method interprets classifier interactions in datasets.
problem Understanding attribute interactions in classification models.
method ASTRID method for investigating attribute interactions.
result ASTRID reveals associations between attributes for better model interpretability.
Framework for fair classification with noisy protected attributes and provable guarantees.
problem Fair classification with noisy protected attributes.
method Optimization framework for linear and linear-fractional fairness constraints, handling multiple non-binary attributes.
result Provably fair classifier with minimal accuracy loss, even with large noise.
Study reveals attributes help in few-shot classification generalization.
problem Understanding what makes some novel classes easier to learn.
method Defined attributes to quantify concept relatedness, used supervised and self-supervised learning.
result Combining self-supervised pretraining with supervised finetuning improves generalization.
A framework to explain decoder-only sequence classification models using intermediate predictions.
problem Explaining predictions of decoder-only sequence classification models.
method Progressive Inference framework with Single Pass-Progressive Inference and Multi Pass-Progressive Inference methods.
result Significantly better attributions compared to prior work on text classification tasks.
This paper surveys and classifies attribute-aware CF models.
problem Rating prediction with user and item attributes.
method Mathematical classification of attribute-aware CF models into four categories.
result Comprehensive comparison of effectiveness among different categories.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
System helps scientists visualize deep learning model of x-ray images.
problem Understanding complex x-ray scattering images with multiple attributes.
method Interactive visualization system in feature space and classification output.
result Users can explore and compare images and attributes flexibly.
DRSVM uses deep learning to rank relative attributes between image pairs.
problem Classifying relative attributes between image pairs.
method Deep Siamese network with rank SVM loss function.
result DRSVM outperforms state-of-the-art methods on multiple datasets.
Study uncovers bias in image classification models using attribution maps.
problem Data bias in image classification models.
method Created an artificial dataset with known bias, trained CNNs, and used attribution maps to inspect decisions.
result Different attribution map techniques highlight bias better than others, and metrics support bias identification.
New framework improves attribution of predictive uncertainties in classification models.
problem Improper attribution of predictive uncertainties in classification tasks.
method Combines path integrals, counterfactual explanations, and generative models.
result Framework outperforms existing alternatives in quantitative evaluations.
New technique clusters and classifies datasets with missing attributes.
problem Clustering and classification issues with incomplete data.
method Modified K-MEANS++, Scalable K-MEANS++, and kNN algorithms using Sentenced Discrepancy Measure (AWPD).
result New algorithms show better results on datasets with missing attributes.
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
AttGAN edits facial attributes by changing only what you want, preserving details.
problem Facial attribute editing with preservation of details.
method Encoder-decoder architecture with attribute classification and reconstruction learning.
result Outperforms state-of-the-arts on realistic attribute editing with preserved details.
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context,…
Method reduces bias in occupation classification without protected attribute data.
problem Mitigating bias in occupation classification without access to protected attributes.
method Uses word embeddings to discourage correlation between predicted occupation probability and name.
result Reduces race and gender biases without significant loss in true positive rate.
New method learns fair representations by separating out protected attributes.
problem Learning fair representations invariant to protected attributes.
method FD-VAE: disentangles latent space into target, protected, and mutual attributes.
result FD-VAE outperforms previous methods in fairness metrics.
Enhances apparel attribute recognition with a two-layer ensemble method.
problem Improving accuracy in apparel attributes classification using deep neural networks.
method Proposes a two-layer mixture framework combining bagging and boosting for ensemble learning.
result The proposed method outperforms individual models and ensemble methods.
Proposes a new method to interpret EEG classification models without needing a baseline.
problem Reliable interpretation of EEG classification models using integrated gradients.
method Compensated Integrated Gradients using Shapley sampling.
result The proposed method provides more reliable attributions than original integrated gradients.
WGNN learns graph representations from incomplete attribute data.
problem Missing node attributes in graphs.
method WGNN learns node representations from decomposed attribute matrices and uses Wasserstein space for message passing.
result WGNN outperforms existing methods in node classification tasks with missing attribute data.
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
problem Missing values in datasets can be lost through imputation, potentially reducing model performance.
method Combines missing-indicator approach with imputation strategies on real datasets.
result Missing-indicators generally increase classification performance, especially for categorical attributes.
MGCN improves multi-layer graph classification using node attributes and relations.
problem Lack of comprehensive multi-layer graph embedding methods considering node attributes and different types of edges.
method Proposes MGCN, a method that combines GCN for multi-layer graphs, incorporating node attributes and both within and between layer relations.
result MGCN outperforms other multi-layer and single-layer methods in semi-supervised node classification tasks.
New framework for fair classification in adversarial settings with provable guarantees.
problem Fairness in classification with adversarial perturbations of protected attributes.
method Optimization framework for learning fair classifiers with provable guarantees.
result Near-tightness of accuracy and fairness guarantees for multiple protected attributes and various hypothesis classes.
New technique reduces bias in DNN models without sensitive attribute annotations.
problem Existing bias mitigation methods require instance-level annotations and do not guarantee removal of all sensitive information.
method Representation Neutralization for Fairness (RNF) debiases only the classification head of DNN models using neutralized representations.
result RNF effectively reduces discrimination of DNN models with minimal performance degradation.
Paper tackles product categorization with structured and unstructured attributes for large-scale eCommerce.
problem Challenges in categorizing products with thousands of classes and millions of products.
method Compares hierarchical and flat models, uses Deep Learning for feature extraction, combines structured and unstructured attributes.
result Flat models perform better in specific cases, and the proposed approach handles faulty attribute names and values.
A new method improves semi-supervised learning by handling tasks with different attribute spaces.
problem Existing methods assume tasks share the same attribute space, limiting their applicability.
method Meta-learning approach that embeds labeled and unlabeled data in task-specific spaces using neural networks.
result Improves test performance on tasks with small labeled data using unlabeled and various task data.
Obtaining compact and discriminative features is one of the major challenges in many of the real-world image classification tasks such as face verification and object recognition. One possible approach is to represent input image on the basis of high-level features that carry semantic meaning which humans can understan…
Study shows squared loss is robust for binary classification with noisy features.
problem Learning binary classifiers with noisy features.
method Sy-De and Asy-In attribute noise models, analyzing l0−1 and lsq losses. result Squared loss is robust in Sy-De model but not in Asy-In model.
Simple graph representation outperforms complex methods in graph classification.
problem Graph classification and representation learning on graphs.
method Developed a simple yet meaningful graph representation and tested its effectiveness.
result Simple graph representation achieves similar performance to state-of-the-art methods for non-attributed graph classification.
Study reveals class-dependent effects in perturbation-based feature attribution metrics for time series classification.
problem Varying effectiveness of perturbation-based metrics across different classes in time series models.
method Systematic empirical analysis across multiple datasets, model architectures, and perturbation strategies.
result Perturbation-based metrics show varying effectiveness across classes, with some metrics performing better for certain classes.
Proposes a stochastic optimization method for feature attribution.
problem Improving feature attribution methods for complex models.
method Reformulates the optimization problem as a differentiable function solvable by gradient-based algorithms, particularly stochastic optimization.
result The proposed method effectively identifies relevant parts of images.
A-DOGE embeds attributed graphs efficiently using density of states.
problem Efficiently represent node-attributed graphs with few numerical features.
method A-DOGE uses density of states to blend topology and attributes, leveraging efficient approximation algorithms.
result A-DOGE achieves competitive performance with modern supervised GNNs while being significantly faster.
Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discusse…
Paper proposes TPathMine model for more accurate user attribute prediction.
problem Predicting user attributes from click data in heterogeneous networks.
method HetPathMine model with meta-path weights optimized for user emotional preferences.
result TPathMine model achieves higher accuracy in user attribute prediction.
Neural-Brane learns vertex embeddings for networks using both topology and attributes.
problem Lack of nodal attributes in network embedding methods.
method Neural Bayesian Personalized Ranking for Attributed Network Embedding.
result Neural-Brane outperforms state-of-the-art methods in node classification and clustering tasks.
Proposes a new method for fairness in machine learning with multiple protected attributes.
problem Ensuring fairness in machine learning models with continuous and multiple protected attributes.
method Distance covariance regularisation framework to mitigate association between model predictions and protected attributes.
result Demonstrates effectiveness in mitigating fairness gerrymandering in regression tasks.
The paper tackles fairness in classification by learning fair latent representations.
problem Fairness issues in classification algorithms used in societally critical domains.
method Develops a minimax adversarial framework to learn fair latent representations.
result The framework provides theoretical guarantees for statistical parity and individual fairness.
Adversarial methods fail to remove demographic attributes from text data.
problem Demographic information of authors is encoded in text-based neural classifiers.
method Adversarial training to remove demographic attributes.
result Post-hoc classifiers trained on encoded sentences still achieve higher accuracies.
StylEx trains a GAN to explain classifier decisions in StyleSpace.
problem Creating meaningful image-specific explanations for classifier decisions.
method Training a StyleGAN to learn a classifier-specific StyleSpace, incorporating the classifier model.
result StylEx finds attributes that align with semantic ones and generates human-interpretable explanations.
Optimal LDP mechanisms reduce data unfairness in classification.
problem Reducing data unfairness in classification models.
method Developed a closed-form optimal mechanism for binary attributes and a tractable framework for multi-valued attributes.
result Optimal LDP mechanisms improve fairness in classification while maintaining accuracy close to non-private models.