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.
New stability measures for similar features improve feature selection accuracy.
problem Existing stability measures fail to distinguish similar features in highly correlated datasets.
method Introduce new adjusted stability measures that consider feature similarities.
result One new stability measure considers highly similar features as interchangeable.
Proposes interpretable set functions for sparse categorical features.
problem Automating the creation of interpretable features from sparse categorical data.
method Deep lattice network model with monotonicity constraints for permutation-invariant feature vectors.
result Achieved similar accuracy to deep sets or neural networks, but with enhanced interpretability.
CDANs classify unordered feature sets efficiently and invariantly.
problem Classifying unordered feature sets with traditional neural networks leads to spurious patterns.
method Convolutional deep averaging networks (CDANs) for permutation-invariant classification.
result CDANs outperform linear embeddings and other methods in classifying unordered feature sets.
Fuzzy Rough Set selects key features for phishing detection.
problem Efficient feature selection for phishing detection.
method Fuzzy Rough Set (FRS) theory for feature selection.
result Maximum F-measure of 95% using Random Forest.
Random small feature subsets outperform FS in diverse datasets.
problem The significance of selected features in high-dimensional datasets is questionable.
method Analysis of 28 diverse datasets (microarray, RNA-Seq, etc.).
result Any arbitrary set of features performs as well as or better than selected features across datasets.
Proposes a new stability measure for model fitting on similar feature data sets.
problem Model fitting on data sets with similar features is challenging.
method Tuning hyperparameters in a multi-criteria fashion with predictive accuracy and feature selection stability.
result Our approach achieves similar or better predictive performance than single-criteria and stability selection approaches.
Deep networks learn spurious features instead of actual object features, leading to poor generalization.
problem Deep networks' poor generalization on out-of-distribution samples due to learning spurious features.
method Evaluation of existing robust feature learning methods and regularization methods, comparison against a baseline designed to capture high correlation features.
result A baseline method trained on high correlation features outperforms existing methods in generalization.
New model predicts sets from feature vectors without discontinuity issues.
problem Discontinuity issues in predicting sets from feature vectors.
method General model that respects set structure, auto-encodes point sets, predicts bounding boxes, and attributes.
result Model successfully predicts sets from a single feature vector without discontinuity.
Active feature selection uses mutual information to choose fewer labels for better feature selection.
problem Selecting features with limited labeled data.
method Uses active feature selection with mutual information criterion, optimizing label selection for better feature quality.
result Algorithm selects features with higher mutual information using fewer labels than the data set size.
FIRES framework selects stable features from online data.
problem Efficiently selecting features in online settings with limited data.
method FIRES framework uses model parameter importance for feature selection.
result FIRES selects stable feature sets with minimal model complexity.
A new method reduces the number of features needed for kernel approximation from cubic to logarithmic.
problem Large datasets make kernel methods computationally expensive and impractical.
method Combines random feature maps with data-dependent feature selection to achieve Nystrom-like performance with fewer features.
result Achieves small kernel matrix approximation error and better test set accuracy with fewer features than state-of-the-art methods.
A novel weighted feature selection method using fuzzy sets improves classification accuracy and stability.
problem Improving feature selection accuracy and stability in machine learning models.
method Combination of four feature selection methods using fuzzy sets and bootstrap.
result Our method achieved significantly higher stability than individual methods.
Study evaluates feature selection methods for emotion recognition in resource-constrained settings.
problem Reducing memory and computational requirements for emotion recognition in low-resource settings.
method Evaluation of three feature selection methods: ILFS, ReliefF, Fisher, and AFS.
result Smaller feature sets can achieve similar or better accuracy, reducing resource usage.
New algorithm enumerates decision tree Rashomon sets for continuous features.
problem Computing Rashomon sets for decision trees with continuous features.
method Developed an algorithm that exactly enumerates decision-tree Rashomon sets while exploiting the ordered structure of continuous features. Introduced a relaxation for approximate enumeration and an anytime algorithm.
result Achieved orders-of-magnitude speedups over existing enumeration methods.
New algorithm selects features using Catastrophe model for better regression analysis.
problem Improving feature selection in regression analysis to enhance model performance.
method The algorithm ranks features based on their fit to the Catastrophe model and uses Akaike information criterion for ranking.
result The proposed algorithm outperforms RELIEF in various datasets.
Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data. Because of this, we believe SVMs are an excellent candidate to guide the development of an analytic feature selection algorithm, as opposed to the more commonly used heuristic methods. We propose a filter-based feature…
Exclusive Group Lasso improves feature selection in correlated biological data.
problem Correlated features hinder Lasso performance in biological classification problems.
method Proposes and solves the exclusive group Lasso, combining stability selection and random group allocation.
result Exclusive Group Lasso outperforms Lasso in comprehensive selection of informative features.
Study improves feature acquisition for static settings in AFAPE.
problem Evaluate AFAPE performance in static feature settings.
method Derive and adapt IPW, DM, and DRL estimators for MAR and MNAR missingness.
result Improved data efficiency in synthetic and real-world experiments.
Pipeline learns topological features for protein stability prediction.
problem Predicting protein stability using topological features.
method Data-driven method to learn topological features, comparing with expert features.
result Topological features achieve 92%-99% of SME-based models' performance.
In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set…
In a regression setting we propose algorithms that reduce the dimensionality of the features while simultaneously maximizing a statistical measure of dependence known as distance correlation between the low-dimensional features and a response variable. This helps in solving the prediction problem with a low-dimensional…
Dataset2Vec learns dataset meta-features without expert knowledge.
problem Learning meta-features for datasets requires expert domain knowledge.
method Dataset2Vec combines DeepSet architecture with hierarchical sets to learn meta-features.
result Meta-features learned by Dataset2Vec outperform engineered features.
This paper uses PCA and FA for feature selection in credit rating.
problem Selecting important features for credit rating prediction.
method Principal Component Analysis and Factor Analysis.
result Factor Analysis reduces feature set significantly without losing much accuracy.
This paper introduces a new feature learning technique based on error representation.
problem Learning high-level features for classification from diverse and imbalanced data.
method Inverse feature learning using error representation approach.
result Significantly better performance compared to state-of-the-art techniques.
There has been a surge in the number of large and flat data sets - data sets containing a large number of features and a relatively small number of observations - due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. I…
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feat…
Enhances machine learning with background knowledge through feature generation.
problem Machine learning struggles with small datasets.
method Generates features from auxiliary datasets not similar to the original task.
result Significant improvement in various learning tasks.
Enhances ZSL models with biologically inspired feature enhancement.
problem Limited training data leads to poor feature extraction from pre-trained models.
method Dual-channel learning framework using auxiliary data sets.
result Improves ZSL model's generalization ability and achieves state-of-the-art results.
New robust algorithms improve learning with feature feedback.
problem Interactive learning with discriminative feature feedback.
method Developed new robust interactive learning algorithms with improved mistake bounds.
result Achieved significantly lower mistake bounds in adversarial and stochastic settings.
The paper tackles feature selection for ordinal regression, considering feature redundancies and privileged information.
problem Discovering relevant factors in ranked data with potentially redundant features and privileged information.
method Develops feature relevance bounds for linear ordinal regression, considering feature redundancies and privileged information.
result Identifies all strongly and weakly relevant features and their type of relevance.
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.
RFSVM with random features achieves faster learning rates.
problem Improving the learning rate of SVM with random features.
method Support Vector Machine with N ≪ m N\ll m N ≪ m random features, optimized feature map, and reweighted feature selection. result RFSVM achieves faster learning rates than O ( 1 / m ) O(1/\sqrt{m}) O ( 1/ m ) under low noise assumptions. theft package simplifies feature extraction for time series analysis in R.
problem Lack of a unified access point and methodological pipelines for feature-based time series analysis.
method theft package provides a unified framework for computing features from six open-source time series feature sets.
result theft enables comprehensive quantification and interpretation of time series structure.
Feature Learning aims to extract relevant information contained in data sets in an automated fashion. It is driving force behind the current deep learning trend, a set of methods that have had widespread empirical success. What is lacking is a theoretical understanding of different feature learning schemes. This work p…
Study compares single vs ensemble feature selection for cancer diagnosis.
problem Identifying relevant variables for cancer diagnosis and prognosis.
method Comparison of single feature selection algorithms and ensemble of diverse algorithms.
result Ensemble approach did not improve predictive performance over individual algorithms.
Learning a distribution conditional on a set of discrete-valued features is a commonly encountered task. This becomes more challenging with a high-dimensional feature set when there is the possibility of interaction between the features. In addition, many frequently applied techniques consider only prediction of the me…
New algorithm improves feature selection for streaming data.
problem Traditional OSFS methods assume all data available at runtime, but features and samples stream concurrently.
method Introduces Geometric Online Adaption (GOA) for concurrent streaming of features and samples.
result GOA outperforms SAOLA on various datasets and in the OSFS-SS setting.
The paper sets communication limits for distributed optimization with feature-based data partitions.
problem Understanding communication limits in distributed convex optimization with feature-based data partitions.
method Developed tight lower bounds on communication rounds for non-incremental and incremental algorithms.
result Established communication limits for a broad class of algorithms under feature-based data partitioning.
Gradient Boosted Feature Selection (GBFS) selects features reliably and scales well.
problem Feature selection in complex datasets.
method Gradient Boosted Trees modification.
result GBFS outperforms other feature selection algorithms on real-world data.
Study shows adding noise to training data improves speech synthesis system's performance under noisy test conditions.
problem Impact of noisy linguistic features on neural network-based speech synthesis systems.
method Comparison of systems using ideal and corrupted linguistic features in training and test sets.
result Adding noise to training data can regularize the model and improve performance under noisy test conditions.
In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N, the n…
The visibility transformation embeds data position into signature features for efficient pattern recognition.
problem Embedding absolute position into signature features for efficient pattern recognition.
method The visibility transformation is put on a theoretical footing and used to embed absolute position into signature features efficiently.
result The generated feature set simplifies pattern recognition by accommodating nonlinear functions of absolute and relative values.
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…
Decision forests learn to model text by evaluating categorical-set conditions.
problem Decision forests cannot directly model text features.
method Defined and learned conditions for categorical-set features, enabling efficient text modeling.
result Decision forests can now directly model text features.
Feature bagging improves stability through random feature subsampling.
problem Improving the stability of ensemble learning methods.
method Introducing feature instability (FI) and analyzing feature bagging in parametric and model-free settings.
result Feature bagging provides stronger stability than non-bagged methods, especially with aggressive subsampling.
Paper introduces robust learning from feature feedback, even with annotator errors.
problem Learning from human feedback on discriminative features, especially when annotators make mistakes.
method Develops a robust framework for learning with imperfect feedback, deriving regret bounds in adversarial and stochastic settings.
result Regret bounds independent of feature number, showing robust learning is not reducible to non-robust settings.
Improved SPSA-FSR method for feature selection and ranking in machine learning.
problem Feature selection and ranking in machine learning.
method Improved Simultaneous Perturbation Stochastic Approximation (SPSA) method with Barzilai and Borwein (BB) method for non-monotone iteration gains.
result Dramatically reduces the number of iterations required for convergence without impacting solution quality.