A new learning method for evolving features in streaming data.
problem Learning with data streams where features can change over time.
method Develops a learning paradigm for feature evolvable streaming data, combining predictions from old and new features.
result Improves performance on new features by leveraging recovered old features.
New approach for feature evolution in streaming data with limited storage.
problem Rarely-provided labels in feature evolving streams.
method Incorporates manifold regularization and a buffer to adapt to different storage budgets.
result Preserves the performance of feature evolving learning across different storage budgets.
DEVDAN adapts to evolving data streams by automatically adding or removing features.
problem Adapting Denoising Autoencoder to rapidly changing data streams.
method DEVDAN features an open structure with an NS method for automatic feature addition or removal.
result DEVDAN improves classification accuracy on non-stationary data streams.
CDLEEDS detects local changes in evolving data streams for accurate feature attributions.
problem Local feature attributions become obsolete in evolving data streams.
method CDLEEDS, a flexible framework for detecting local change and concept drift.
result CDLEEDS reliably detects both local and global concept drift.
Proposes standards for evaluating online machine learning methods in evolving data streams.
problem Difficulty in evaluating online machine learning methods under realistic conditions.
method Proposes comprehensive evaluation standards, performance measures, and evaluation strategies.
result Provides a new Python framework (float) for modular integration of libraries and custom code.
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
problem Concept drift in evolving data streams.
method Adapts XGB to update the ensemble with new data, maintaining consistency with current concept.
result Improves classification accuracy on evolving data streams compared to other methods.
SDF adapts Deep Forest for evolving data streams with active learning.
problem Adapting Deep Forest for evolving data streams.
method Streaming Deep Forest (SDF) with Augmented Variable Uncertainty (AVU) active learning.
result SDF with AVU outperforms other methods trained with all instances by 70% labeling budget.
DEVDAN adapts to changing data streams by dynamically adding and removing hidden units.
problem Fixed DAE network capacity limits adaptability to rapidly changing environments.
method DEVDAN features an open structure with dynamically adjustable hidden units.
result DEVDAN outperforms state-of-the-art methods on ten datasets.
A framework selects the best (classifier, detector) pair for evolving data streams.
problem Selecting the best (classifier, detector) pair for data streams evolving over time.
method Reservoir of diverse adaptive learners and stacking fast Hoeffding drift detection methods.
result The best (classifier, detector) pair evolves as the stream evolves and is selected by the framework.
Differentially private ensemble classifiers adapt to data streams while protecting privacy.
problem Adapting to evolving data characteristics while protecting private information.
method Unbounded ensemble updates, model agnostic approach.
result Outperforms competitors on various privacy, drift, and distribution settings.
NADINE builds MLPs from streaming data, overcoming forgetting issues.
problem Building deep neural networks from streaming data efficiently and avoiding forgetting.
method NADINE uses a fully open MLP structure that dynamically evolves its depth and width online, resolving catastrophic forgetting through soft-forgetting and adaptive memory.
result NADINE outperforms existing methods in nine data stream classification and regression problems.
ParsNet tackles weakly supervised data streams with a self-evolving deep neural network.
problem Weakly supervised data streams hinder existing data stream algorithms.
method ParsNet uses a self-labelling strategy with hedge (SLASH) and a closed-loop configuration of generative and discriminative training processes.
result ParsNet outperforms other methods in high-dimensional data streams and infinite delay simulations.
Dynamic Model Tree improves online learning for evolving data streams.
problem Effective and transparent machine learning on data streams is challenging.
method Revisit Model Trees for data stream applications, introducing Dynamic Model Tree.
result Dynamic Model Tree reduces the number of splits and outperforms state-of-the-art models.
New method rebalances evolving data streams incrementally.
problem Incremental rebalancing of evolving data streams.
method Proposes a new streaming approach for rebalancing data streams online.
result Outperforms existing approaches in rebalancing data streams.
Signatures simplify analysis of evolving data streams.
problem Understanding and analyzing irregular, non-stationary data streams.
method Mathematical signatures reduce noise and preserve key information.
result Signatures manage the exponential scaling of data complexity.
We bring the theory of rough paths to the study of non-parametric statistics on streamed data. We discuss the problem of regression where the input variable is a stream of information, and the dependent response is also (potentially) a stream. A certain graded feature set of a stream, known in the rough path literature…
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
Unsupervised learning for evolving data streams with STAM architecture.
problem Learning from non-stationary, unlabeled data streams over time.
method Self-Taught Associative Memory (STAM) architecture with online clustering, novelty detection, and feature storage.
result STAM architecture improves clustering and classification tasks compared to existing continual learning models.
ATL learns from many streaming processes without labeled data.
problem Knowledge transfer across many streaming processes with covariate shift and drifts.
method Autonomous transfer learning with generative and discriminative phases, KL divergence optimization, and elastic network structure.
result Improved performance and faster training speed compared to existing methods.
Framework tracks evolving news stories across multiple sources.
problem Tracking evolving news stories across diverse sources and formats.
method Cross-domain story tracking approach using entity graphs and learning-to-rank.
result Outperforms state-of-the-art methods for real-time story tracking.
Probabilistic hash embeddings improve online learning of categorical features.
problem Online learning of categorical features with changing vocabulary.
method Probabilistic hash embedding (PHE) with Bayesian online learning.
result PHE mitigates forgetting and maintains high performance in online settings.
Approach to detect and adapt to concept drift in unlabeled streaming data.
problem Detect and adapt to concept drift in high-dimensional, noisy, low-context data.
method Density-based clustering for virtual drift and weak supervision for real drift.
result 90% precision in detecting and adapting to concept drift for 4 years after initial deployment.
Paper tackles unpredictable feature evolution in learning.
problem Learning with unpredictable feature evolution.
method Proposes PUFE method to fill incomplete overlapping period and formulate as matrix completion problem. Uses ensemble method to incorporate old and new feature spaces.
result Theoretical and experimental validation shows PUFE method can always follow the best base models.
TSK-Streams learns fuzzy rules from data streams.
problem Adaptive learning from evolving data streams.
method Combines AMRules principles with fuzzy rule advantages.
result TSK-Streams performs highly competitively in experiments.
Study examines AutoML adaptation to evolving data.
problem Understanding and improving AutoML performance with concept drift.
method 6 concept drift adaptation strategies evaluated on various AutoML approaches.
result Robust AutoML techniques can be developed to handle concept drift.
COLoKe adapts Koopman embeddings online, reducing overfitting and improving long-term predictions.
problem Online adaptation of Koopman embeddings to avoid overfitting and maintain long-term predictive accuracy.
method Combines deep feature learning with multistep prediction consistency in a lifted space, using a conformal-style mechanism for selective updates.
result Empirically effective in reducing overfitting and maintaining long-term predictive accuracy.
New features capture the order of data streams.
problem Handling ordered moments in massive data streams.
method Introducing features for ordered moments.
result Theoretical guarantees for learning algorithms.
EGFC learns from streaming data to classify power quality disturbances.
problem Real-time detection and classification of power quality disturbances.
method Evolving Gaussian Fuzzy Classification (EGFC) framework with semi-supervised learning.
result Encouraging classification results from online data streams.
DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.
problem Handling concept drift in evolving data streams.
method DiwE measures diversity based on regional distribution disagreement and uses it to weight instances and select classifiers.
result DiwE outperforms other algorithms on various synthetic and real-world data stream benchmarks.
A new algorithm for K-means clustering in evolving data streams.
problem Clustering of continuously arriving data in streaming scenarios with concept drift.
method Formal definition of Streaming K-means, surrogate error function, algorithm for minimizing surrogate error. result The surrogate error function effectively approximates the Streaming K-means error. Sketches linear classifiers using Weight-Median Sketch for efficient data stream analysis.
problem Efficiently learning and analyzing data streams with limited memory.
method Introduces Weight-Median Sketch for compressed linear classifier learning over data streams.
result Memory-limited execution of various analyses over streams, including feature selection and mutual information estimation.
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem …
Method learns Bayesian networks from distributed streaming data with reduced communication.
problem Learning and maintaining machine learning models over distributed, streaming data.
method Communication-efficient method for continuously learning Bayesian networks over a distributed stream.
result Exponential reduction in communication compared to baseline approaches.
Develops online cluster validation indices for streaming data.
problem Validating clusters in streaming data.
method Two online versions of Xie-Beni and Davies-Bouldin indices, analyzed using sk-means and online ellipsoidal clustering.
result Incremental Xie-Beni index with forgetting factor superior to other indices.
AdapVAE learns streaming data clustering and feature learning adaptively.
problem Adaptive clustering and feature learning for streaming data.
method Bayesian Nonparametric (BNP) modeling with Deep Neural Networks (DNNs) for feature learning, online variational inference algorithm.
result AdapVAE can adaptively detect novel clusters in emerging data without catastrophic forgetting.
Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time information. Learning algorithms deployed in such contexts must rely on single-pass inference, where the data history is never revisited. In streaming contexts, learning must also …
Proposes methods to make data streams fair without fixing a model.
problem Fairness of data-driven models in evolving data streams.
method Modifies input data to ensure fair outcomes for any classifier.
result Improves predictive performance and low discrimination scores over time.
The study creates user personas based on user tenure and behavior for VoD streaming.
problem Understanding evolving user behavior in streaming services without explicit user profiles.
method Construct user personas using tenure timelines and temporal behavioral features.
result Personas provide stable and interpretable insights into user behavior evolution.
This paper analyzes diversity measures for streaming data ensembles.
problem Understanding diversity measures for streaming data ensembles.
method Theoretical analysis of diversity measures for streaming data ensembles.
result Analysis provides deeper understanding of diversity and its impact on online ensemble learning.
A novel approach for cost-sensitive feature acquisition in data streams.
problem Learning scenarios with feature acquisition at a cost.
method Incremental feature acquisition using a context-aware feature-value function in a reinforcement learning paradigm.
result The proposed method efficiently acquires features and makes accurate predictions.
DeepStreamCE detects new classes in streaming deep neural networks.
problem Detecting new classes in deep neural networks in a streaming environment.
method Uses autoencoder and MCOD stream-based clustering for real-time concept evolution detection.
result DeepStreamCE outperforms OpenMax in identifying concept evolution.
LUNAR uses cellular automata for real-time data classification in fast streams.
problem Real-time machine learning challenges with fast data streams and concept drift.
method Streamified cellular automata approach for incremental learning and adaptation.
result Competitive performance in classification compared to established online learning methods.
MDDM detects concept drifts in evolving data streams using McDiarmid's inequality.
problem Concept drift in rapidly evolving data streams causes inaccurate models.
method Sliding window with weights, comparing weighted means over time.
result MDDM detects concept drifts with shorter delays and lower false negatives.
Online learning algorithm identifies contextual changes for streaming data.
problem Challenges in identifying and utilizing contextual cues for streaming data.
method Neural network-based autoencoder for identifying contextual changes; comparison to a knowledge base of learned contexts.
result Classifier training benefits from automatically discovered contexts, demonstrating quicker learning convergence during contextual changes.
LOFS library aids in online streaming feature selection.
problem Sequentially adding dimensions in high-dimensional data.
method State-of-the-art algorithms for online streaming feature selection.
result First open-source library for online streaming feature selection.
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.
EML model tackles evolving features in online metric learning.
problem Challenges in applying metric learning to evolving features.
method Develops a new Evolving Metric Learning (EML) model for incremental and decremental features.
result EML model handles instance and feature evolutions simultaneously.
Paper classifies multiple video sources in encrypted tunnels using NLP-inspired features.
problem Traffic classification in encrypted video streams.
method Deep learning with a novel NLP-inspired feature for multi-label classification.
result The method achieves high performance on binary and multilabel classification tasks.