Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
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48 results for “online feature selection”
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets t…
OOMP selects features online for sparse linear regression.
problem Feature selection in high-dimensional sparse linear models.
method Online algorithm that alternates between feature selection and coefficient estimation.
result Theoretical guarantees and computational complexity analysis of OOMP.
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.
Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when …
Online feature selection has been an active research area in recent years. We propose a novel diverse online feature selection method based on Determinantal Point Processes (DPP). Our model aims to provide diverse features which can be composed in either a supervised or unsupervised framework. The framework aims to pro…
New algorithm learns reliable regression coefficients from streaming data with partial features and adversarial corruption.
problem Learning reliable regression coefficients from streaming data with partial features and adversarial corruption.
method RoOFS algorithm that iteratively updates regression coefficients and uncorrupted feature set via robust online feature substitution.
result RoOFS algorithm has a restricted error bound compared to the optimal solution and outperforms existing methods in feature selection and regression coefficient recovery.
New online feature selection method handles streaming data with concept drift.
problem Handling streaming data with concept drift and sparsity.
method Online feature screening method with model adaptation.
result Online screening methods with model adaptation outperform without model adaptation on data streams with concept drift.
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.
Current online learning methods suffer issues such as lower convergence rates and limited capability to select important features compared to their offline counterparts. In this paper, a novel framework for online learning based on running averages is proposed. Many popular offline regularized methods such as Lasso, El…
Automatically assesses the quality of online health articles.
problem Lack of automated tools to evaluate the quality of online health information.
method Data mining approach using 10 quality criteria and feature selection.
result Classifier achieved 84%-90% accuracy on 10 criteria.
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.
mRMR method selects relevant features for marketing models without redundancy.
problem Selecting relevant features from large feature spaces in machine learning.
method Extends mRMR framework with non-linear redundancy and model-based relevance measures.
result Implemented mRMR method in production for Uber's marketing machine learning platform.
Improved online penalty selection for time series models.
problem Efficiently selecting penalty parameters for lasso in time series models.
method Enhanced autoregressive model with online penalty selection.
result Significantly improved computational performance and forecast accuracy.
Fast feature selection for SHM using canonical correlation.
problem Feature selection for structural health monitoring.
method Greedy search of sum of squared canonical correlation coefficients.
result Extremely fast feature selection with good performance.
We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co…
Paper tackles online budgeted learning for feature acquisition in machine learning.
problem Finding optimal feature values to acquire from each instance in a data stream under budget constraints.
method Introduces a general framework for online budgeted learning, proposing two feature value acquisition policies: random and adaptive.
result Adaptive policies outperform random policies for most budget limitations and datasets, achieving near-optimal results in some cases.
Optimizes recommender selection online with D-optimal design.
problem Finding the optimal recommender in online exploration-exploitation.
method Leverages D-optimal design from statistics to maximize information gain.
result Achieves maximum information gain during online exploration.
We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to f…
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicatorsq-fin.ST
Study predicts stock mid-prices using machine learning with selected features.
problem Predicting mid-price movements in stock markets.
method Wrapper feature selection using entropy, LMS, LDA, and adaptive logistic regression.
result Best performance achieved with a small set of advanced features.
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.
Online reviews predict long-term stock returns.
problem Little research on predicting individual stock returns using online reviews.
method Selected 6,246 features from 18 million reviews; built prediction models with cross-validation.
result Achieved 13.94% accuracy improvement over existing methods.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
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.
Paper uses PSM to improve fake news detection generalizability.
problem Confounding variables in fake news features.
method Propensity Score Matching (PSM) to select features.
result Generalizability of fake news detection methods improved significantly.
Two novel methods identify influential features in CMABs for better reward distribution.
problem Suboptimal features degrade rewards, interpretability, and efficiency in CMABs.
method Heterogeneous Incremental Effect (HIE) and Heterogeneous Distribution Divergence (HDD) methods.
result Consistent ability to identify influential HTE features, enhancing CMAB performance.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.
For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of feat…
Online method selects candidates from data streams, ensuring irreversible decisions.
problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.
With the deployment of online monitoring systems in distribution networks, massive amounts of data collected through them contains rich information on the operating states of the networks. By leveraging the data, an unsupervised approach based on bidirectional generative adversarial networks (BiGANs) is proposed for op…
CAP algorithm controls FCR in online selective prediction.
problem Online predictive tasks with temporal multiplicity and FCR control.
method CAP framework with adaptive pick rule and calibration set construction.
result CAP achieves exact selection-conditional coverage guarantee and FCR control.
We propose an in-depth study of lending behaviors in Kiva using a mix of quantitative and large-scale data mining techniques. Kiva is a non-profit organization that offers an online platform to connect lenders with borrowers. Their site, kiva.org, allows citizens to microlend small amounts of money to entrepreneurs (bo…
Algorithm selects k arms from context-dependent options using Plackett-Luce model.
problem Selecting k arms from context-dependent options with Plackett-Luce feedback.
method Proposes CPPL algorithm inspired by UCB, evaluated on synthetic and real data.
result Demonstrates effectiveness of CPPL algorithm in online algorithm selection.
Inferring the structure of gene regulatory networks (GRN) from gene expression data has many applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy. In t…
We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model. This enables memory-limited execution of several statistical analyses over streams, including online featu…
Designs an online selective sampling approach for choosing which model to use.
problem Active model selection for pre-trained classifiers in unlabeled data streams.
method Online selective sampling approach to query and label examples.
result High probability of outputting the best model with minimal label queries.
Joint sparsity offers powerful structural cues for feature selection, especially for variables that are expected to demonstrate a "grouped" behavior. Such behavior is commonly modeled via group-lasso, multitask lasso, and related methods where feature selection is effected via mixed-norms. Several mixed-norm based spar…
New active learning method for kernel selection improves efficiency and accuracy.
problem Real-world applications where acquiring true labels is costly or time-consuming.
method Active Multiple Kernel Learning (AMKL) with adaptive kernel selection (AMKL-AKS).
result AMKL-AKS achieves optimal sublinear regret and better performance with fewer labeled data.
This work tackles online memory selection in continual learning using information theory.
problem Online selection of a representative replay memory from data streams.
method Information-theoretic criteria (surprise, learnability) and Bayesian model for efficient computation.
result InfoRS improves robustness against data imbalance compared to reservoir sampling.
Proposes MEED framework for model interpretation.
problem Improving model interpretability and avoiding undesired characteristics.
method Adversarial Infidelity Learning (AIL) for effective feature selection.
result AIL mechanism helps learn desired conditional distribution.
The Mondrian Kernelstat.ML
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a…
L2SC improves spectral clustering performance by selectively transferring knowledge across tasks.
problem L2SC tackles the challenge of incorporating new spectral clustering tasks without relearning all previous tasks.
method L2SC uses an orthogonal basis library and feature embedding library to selectively transfer knowledge from previously learned tasks to new tasks.
result L2SC outperforms state-of-the-art spectral clustering algorithms on real-world benchmark datasets.
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
Study builds tools to detect misinformation in online medical videos.
problem Misleading information in online medical videos.
method Manual annotation of a dataset, use of linguistic, acoustic, and user engagement features for classification models.
result Automatic models can identify misinformation with up to 74% accuracy.
Framework for renewable energy forecasting and feature engineering.
problem Forecasting and feature extraction for multivariate processes in renewable energy.
method Derivative-free optimization, ensemble of sequence-to-sequence networks, additive resampling, Bootstrap aggregating.
result The proposed method outperforms other machine learning techniques in long-term forecasts and feature selection.
AutoFIS automatically selects important feature interactions for CTR prediction models.
problem Manual feature interaction design is inefficient and prone to noise.
method Two-stage algorithm: search stage relaxes feature interactions to continuous parameters, re-train stage refines model performance.
result AutoFIS significantly improves CTR and CVR of FM-based models.
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm …