Improved text classification using human-understandable features.
problem Text classification accuracy with traditional methods.
method Solicited human-comprehensible features from a teacher.
result Models with human-comprehensible features are competitive with traditional methods.
A genetic algorithm-based method extracts features for epilepsy EEG classification.
problem Classifying epileptic EEG signals for accurate diagnosis.
method GAFDS method using genetic algorithm for frequency-domain feature search and optimization.
result GAFDS features improve classification accuracy compared to nonlinear features.
Proposes on-the-fly joint feature selection and classification for time-sensitive decisions.
problem Online feature selection and classification for time-sensitive decision making.
method Proposes a framework for joint feature selection and classification on-the-fly, optimizing feature usage and classification accuracy.
result Demonstrates superior performance over state-of-the-art methods on various datasets.
Study shows topological features improve time series classification.
problem Classifying stochastic processes with varying noise and sampling.
method Topological data analysis features compared to statistical and raw features.
result Topological features lead to better classification performance.
Directly compute classification by learning features with class scores.
problem Classification efficiency and accuracy on various datasets.
method PCA for feature encoding, supervised learning model with encoder-decoder structure.
result Effective classification performance on multiple datasets.
Metalearning improves feature selection for faster classification.
problem Optimizing feature selection for various problems.
method Integrates quality-based feature selection into metalearning, estimating feature quality across related problems.
result Metalearning provides significant speedup over standard feature selection methods in supervised text classification.
New method classifies patients with kidney transplant based on many features.
problem Classifying patients with many features (ultrahigh-dimensional data).
method Multivariate screening and classification method leveraging feature correlations.
result Achieves optimal misclassification rates and more powerful discovery.
The paper explores feature selection for improving classification accuracy in event logs.
problem Improving machine learning-based interactive root cause analysis for business process instances.
method Developed structural features from event logs and compared six feature selection algorithms.
result Feature selection can improve classification accuracy without significantly increasing run-time.
DEFRAG accelerates extreme classification by reducing feature dimensions.
problem High precision and scalability in assigning labels from a vast label space.
method Adaptive feature agglomeration to reduce feature dimensions.
result Significant reduction in training and prediction times (up to 40%) for extreme classification algorithms.
A new algorithm efficiently selects features for functional data classification.
problem Feature selection and classification of functional data in high-dimensional spaces.
method Developed a novel optimization problem integrating logistic loss and functional features. Employed functional principal components and a new adaptive Dual Augmented Lagrangian algorithm for efficient minimization.
result FSFC outperforms other methods in computational time and classification accuracy.
Dilated CNN improves multivariate time series classification.
problem Multivariate time series classification.
method Transformed multivariate time series into image-like style, applied dilated and strided convolutions.
result Automatic features extracted by dilated CNN are as effective as hand-crafted features.
Proposes PFCVMLP for feature selection and classification.
problem Performance degradation and low efficiency of traditional sparse Bayesian classifiers in high-dimensional data.
method Sparse Bayesian embedded feature selection method using truncated Gaussian distributions.
result PFCVMLP improves classification performance and feature selection effectiveness.
A method selects key genes from tumor transcriptomics data using kernel methods and improves classification performance.
problem Feature selection for tumor classification using gene expression data.
method Multiple Kernel Learning with latent regularization and non-linear dimensionality reduction.
result Improved tumor classification performance on unseen test samples.
PFDL improves deep learning models' OOD generalization by decorrelating feature embeddings.
problem Out-of-distribution generalization in deep learning models.
method PFDL algorithm that optimizes feature decomposition network and image classification model.
result PFDL improves the accuracy of image classification models on OOD datasets.
A new method learns features for one-class classification using intra-class splitting.
problem Challenges in one-class classification due to limited normal class samples.
method Intra-class splitting and joint training of typical and atypical samples with loss functions.
result The method outperforms other models in one-class classification tasks.
Study shows exponential convergence in classification errors using random features and SGD.
problem Scalability issues in kernel methods for large datasets.
method Binary classification problem with random features and stochastic gradient descent.
result Exponential convergence rate of expected classification error achieved.
New model combines shape and feature-based measures for better time series classification.
problem Limited approaches in time series classification lead to poor results for some classes.
method Proposes a new model that automatically decides between shape and feature-based measures.
result Improves classification accuracy statistically significantly on real-world datasets.
Beam search improves feature selection for better model performance.
problem Improving feature selection for better model performance.
method Proposed beam search as a generalization of forward selection for feature selection.
result Beam search can outperform forward selection, especially with correlated features.
Random Fourier features classification achieves fast learning rates with fewer features.
problem Improving classification efficiency with fewer features.
method Utilizing Lipschitz continuous loss functions and regularity conditions, the study reduces the number of features required for classification.
result Random Fourier features classification can achieve O ( 1 / n ) O(1/\sqrt{n}) O ( 1/ n ) learning rate with only Ω ( n log n ) Ω(\sqrt{n} \log n) Ω ( n log n ) features. This paper improves land cover classification using global spatial features in CNN.
problem Limited classification accuracy and universality of traditional remote sensing image classification methods.
method Integrates global spatial features into a dual-branch CNN for hyperspectral/SAR imagery classification.
result The proposed method outperforms traditional single-channel CNN methods.
This research improves HC classification speed and memory usage with feature selection.
problem Large-scale HC datasets with high-dimensional features.
method Filter-based feature selection methods for dimensionality reduction.
result Up to 3x speed-up and 45% less memory usage on massive datasets.
A new classification method using class-specific features for improved text categorization.
problem Improving text categorization accuracy by leveraging class-specific features.
method EEF classifier based on class-specific features and optimal Bayesian classification rule.
result The proposed EEF classifier outperforms conventional methods on real-life data sets.
Combines static and dynamic features for better multivariate sequence classification.
problem Improving classification performance using both static and dynamic features.
method Generative models (HMM and LSTM) extract temporal information from dynamic data. Static features are combined with the extracted temporal information.
result Hybrid approach outperforms other methods on public datasets.
New method extracts radio signal features for automatic modulation classification.
problem Challenges in automatic modulation classification without expert-defined features.
method Biologically-inspired regularized stacked sparse denoising autoencoders (SSDAs).
result Correct classification rates > 99% at 7.5 dB SNR and > 92% at 0 dB SNR.
GA optimizes EEG feature selection for BCI systems, improving classification accuracy.
problem Finding optimal EEG features for accurate classification in BCI systems.
method Genetic Algorithm (GA) for feature selection and classifier optimization.
result Katz fractal feature with LDA yields highest fitness value.
Improved object classification using neural networks with known and unknown features.
problem Improving classification accuracy for objects described by both known and unknown features.
method Modernized Informational Neurobayesian Approach with consideration of unknown features.
result The method completely solved the problem of misclassification for queries with combining known and unknown features.
A novel feature selection method for SVM improves model accuracy and interpretability.
problem Feature selection in nonlinear SVM classification problems.
method Embedded min-max optimization problem, leveraging duality theory.
result Improves model accuracy and interpretability on benchmark data sets.
Proposes LM3FE for multi-modal feature extraction in image classification.
problem High-dimensional features and multi-modal data challenges.
method Large margin multi-modal multi-task feature extraction (LM3FE) framework.
result LM3FE outperforms single-task feature extraction and multi-modal feature extraction.
Paper proposes a new method to learn features from error representations.
problem Learning from error representations in machine learning.
method Inverse feature learning (IFL) based on deep clustering.
result IFL leads to improved performance in classification and clustering.
Improved image classification accuracy on CIFAR-10 dataset.
problem Classifying images from the CIFAR-10 dataset with high accuracy.
method Combining features from manual and deep learning approaches, including VGG16, Inception ResNet v2, HOG, and pixel intensities.
result Achieved 94.6% testing accuracy by combining top 1000 principal components.
Study compares six feature sets and three baselines for time-series classification.
problem Comparing feature sets for time-series classification tasks.
method Normalization-based approach to benchmarking, comparing 124 problems.
result Feature sets perform similarly overall, with tsfresh showing strongest performance.
Optimal resource allocation improves feature classification accuracy in noisy conditions.
problem Improving feature classification accuracy when features are noisy and resource allocation affects noise magnitude.
method Developed a method for computing optimal resource allocation in various scenarios.
result Non-uniform resource allocation can significantly enhance classification performance.
A new method extracts features from time series data using iterated sums and improves classification accuracy.
problem Time series classification challenges.
method Feature extraction using iterated-sums signature (ISS) followed by a linear classifier.
result Competitive with state-of-the-art methods on UCR archive.
Improved classification with costly features using deep reinforcement learning.
problem Optimizing classification error with limited and costly feature acquisition.
method Revisited Q-learning approach with neural network approximation for sequential feature requests and classification decisions.
result Deep reinforcement learning approach comparable to state-of-the-art algorithms, robust across datasets.
Adversarial model improves implicit relation classification without explicit connectives.
problem Lack of explicit connectives makes implicit discourse relation classification challenging.
method Feature imitation framework with adversarial training.
result State-of-the-art performance on PDTB benchmark.
MOANOFS tackles online feature selection for big data classification.
problem Online supervised feature selection for binary classification in big data.
method Hybrid of online learning and automated negotiation.
result MOANOFS achieves high accuracy with real-world applications.
Genetic Programming constructs features for physics experiments, improving classification accuracy.
problem Lack of interpretable feature construction for experimental physics.
method Combining Genetic Programming with dimensional consistency constraints.
result Constructed features improve classification accuracy by a significant margin.
Dual adversarial co-learning improves multi-domain text classification.
problem Improving text classification across multiple domains.
method Dual adversarial co-learning with shared-private networks and dual adversarial regularizations.
result Achieves state-of-the-art performance on multi-domain sentiment classification datasets.
Paper tackles class-incremental time series classification with dual-stream feature extraction.
problem Class-incremental continual learning for multivariate time series data.
method Dual-stream feature extraction pipeline combining deep temporal embedding features and statistical features.
result Competitive average accuracy across multiple datasets with low forgetting rates.
A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with N N N sets of observations, where each set is labeled with class information, and the prediction of a class label is performed a…
New measure of feature influence in classification problems considering feature dependencies.
problem Measuring the influence of features in classification problems with dependencies.
method Developed a new measure based on cooperative game theory, providing axiomatic characterization and demonstrating its equivalence to the Banzhaf-Owen value.
result The proposed influence measure effectively characterizes feature importance in classification problems with feature dependencies.
Paper uses RL and DCAE to classify large unstructured data with fewer features.
problem Classifying large unstructured data with high precision using fewer features.
method Deep Convolutional Autoencoder (DCAE) for feature learning and Double DQN/Retrace RL algorithms for policy optimization.
result The approach achieves high classification precision with fewer features than traditional methods.
Graph cross network improves graph classification accuracy.
problem Improving graph classification accuracy.
method Graph cross network (GXN) with vertex infomax pooling (VIPool) and feature-crossing layer.
result Improves graph classification accuracy by 2.12% and 1.15%.
The paper proposes a method to automatically discover effective spatial filters for hyperspectral image classification.
problem Discovering an effective set of spatial filters for hyperspectral image classification problems.
method An active set feature learner that includes only features improving the classifier, using multiclass logistic classification.
result A simple classifier can reach state-of-the-art performance with learned filters.
Incorporating feature selection into a classification or regression method often carries a number of advantages. In this paper we formalize feature selection specifically from a discriminative perspective of improving classification/regression accuracy. The feature selection method is developed as an extension to the r…
A fast feature selection method using OLS and SOCC for classification.
problem Feature selection for linear classification.
method Orthogonal Least Squares (OLS) with Squared Orthogonal Correlation Coefficient (SOCC).
result The proposed method outperforms other feature selection methods in speed and accuracy.
New network learns image features inductively for disease classification.
problem Pre-processing image features limits network optimization.
method Inductive end-to-end learning with CNN and graph filters trained jointly.
result Significantly improved classification scores and higher stability.