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- ArticleMay 2024
DEAL: Data-Efficient Active Learning for Regression Under Drift
Advances in Knowledge Discovery and Data MiningMay 2024, Pages 188–200https://doi.org/10.1007/978-981-97-2266-2_15AbstractCurrent work on Active Learning (AL) tends to assume that the relationship between input and target variables does not change, i.e., the oracle is static. However, oracles can be stream-like and exhibit concept drift, which requires updating the ...
- ArticleJuly 2024
Continual AE-WGAN for Unsupervised Anomaly Detection in Streaming Data
Intelligent Information and Database SystemsApr 2024, Pages 3–14https://doi.org/10.1007/978-981-97-4982-9_1AbstractIn today’s data-driven world, the timely detection of anomalies in streaming data is of paramount importance across diverse domains, including cybersecurity, industrial processes, and notably, finance. In the financial sector, where transactions ...
- research-articleJune 2024
Hybrid Firefly Optimised Ensemble Classification for Drifting Data Streams with Imbalance
Knowledge-Based Systems (KNBS), Volume 288, Issue CMar 2024https://doi.org/10.1016/j.knosys.2024.111500AbstractClassification learning on non-stationary data may face dynamic changes from time to time. The major problem lies in addressing the imbalance among classes and the substantial cost associated with labeling instances, especially in the presence of ...
- research-articleJuly 2024
Online Detection and Adaptation of Concept Drift in Streaming Data Classification
Procedia Computer Science (PROCS), Volume 235, Issue C2024, Pages 2803–2811https://doi.org/10.1016/j.procs.2024.04.265AbstractThe dynamism of our digital universe systems presents a key challenge for predictive analytics. Ensuring the model’s ability to generalize beyond the training data is crucial for reliable predictions. Recent research has primarily focused on ...
- research-articleJuly 2024
Impact analysis of real and virtual concept drifts on the predictive performance of classifiers
Procedia Computer Science (PROCS), Volume 235, Issue C2024, Pages 617–627https://doi.org/10.1016/j.procs.2024.04.061AbstractIn the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are ...
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- research-articleMay 2024
A Novel credit card fraud detection feature selection system using machine learning
- Jitendra Kumar Chaudhary,
- Balakrishnan S,
- Dhiraj Kapila,
- Dler Salih Hasan,
- Mohammad Shafeeq,
- Sunil Kumar Vishwakarma
ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine IntelligenceNovember 2023, Article No.: 41, Pages 1–5https://doi.org/10.1145/3647444.3647867Abstract: A comprehensive overview and implementation of a credit card fraud detection system using machine learning for feature selection system. In an era where digital transactions have become the norm, the need for robust fraud detection mechanisms ...
- ArticleNovember 2023
Depth and Width Adaption of DNN for Data Stream Classification with Concept Drifts
Intelligent Data Engineering and Automated Learning – IDEAL 2023Nov 2023, Pages 406–417https://doi.org/10.1007/978-3-031-48232-8_37AbstractTo handle data stream classification with concept drifts, recent studies have shown that a continuously evolving network structure can achieve better performance. However, firstly, they only change one hidden node at a time, which is not enough to ...
- ArticleNovember 2023
A Novel Approach of Adpative Window 2 Technique and Kalman Filter- “KalADWIN2” for Detection of Concept Drift
AbstractA recommendation engine (RE) is a machine learning technique that provides personalized recommendations and anticipates a user's future preference for a collection of goods or services. In Online Supervised Learning (OSL) settings like various REs,...
- ArticleSeptember 2023
Adaptive Reservoir Neural Gas: An Effective Clustering Algorithm for Addressing Concept Drift in Real-Time Data Streams
Artificial Neural Networks and Machine Learning – ICANN 2023Sep 2023, Pages 152–166https://doi.org/10.1007/978-3-031-44223-0_13AbstractsThe concept drift phenomenon describes how the statistical properties of a data distribution change over time. In cybersecurity domain, where data arrives continuously and rapidly in a sequential manner, concept drift can be a significant ...
- ArticleSeptember 2023
Online Deep Hybrid Ensemble Learning for Time Series Forecasting
Machine Learning and Knowledge Discovery in Databases: Research TrackSep 2023, Pages 156–171https://doi.org/10.1007/978-3-031-43424-2_10AbstractThe complex and changing nature of time series data renders forecasting one of the most challenging tasks in time series analysis. It is also commonly acknowledged that no single ML model can be perfectly appropriate for all applications. One ...
- ArticleSeptember 2023
DyLoPro: Profiling the Dynamics of Event Logs
AbstractModern business processes are often characterized by continuous change, which can lead to bias in the results of process mining techniques that assume a static process. This bias is caused by concept drift, which can manifest in many forms and ...
- ArticleSeptember 2023
Model Performance Prediction: A Meta-Learning Approach for Concept Drift Detection
Hybrid Artificial Intelligent SystemsSep 2023, Pages 51–62https://doi.org/10.1007/978-3-031-40725-3_5AbstractData stream applications in highly dynamic environments often face concept drift problems, a phenomenon in which the statistical properties of the variables change over time, which can degrade the performance of Machine Learning models. This work ...
- ArticleSeptember 2023
Financial Distress Prediction in an Imbalanced Data Stream Environment
Hybrid Artificial Intelligent SystemsSep 2023, Pages 168–179https://doi.org/10.1007/978-3-031-40725-3_15AbstractCorporate bankruptcy predictions are crucial to companies, investors, and authorities. However, most bankruptcy prediction studies have been based on stationary models, and they tend to ignore important challenges of financial distress like data ...
- research-articleSeptember 2023
AdaBoost based hybrid concept drift detection approach for mental health prediction
IC3-2023: Proceedings of the 2023 Fifteenth International Conference on Contemporary ComputingAugust 2023, Pages 345–351https://doi.org/10.1145/3607947.3608020One of the most crucial components of reducing the risk of serious mental disease is mental health prediction. Meanwhile, the department of public health may apply mental health prediction as a theoretical foundation to develop plans for behavioral ...
- ArticleSeptember 2023
Extending Drift Detection Methods to Identify When Exactly the Change Happened
Advances in Computational IntelligenceJun 2023, Pages 92–104https://doi.org/10.1007/978-3-031-43085-5_8AbstractData changing, or drifting, over time is a major problem when using classical machine learning on data streams. One approach to deal with this is to detect changes and react accordingly, for example by retraining the model. Most existing drift ...
- ArticleJune 2023
The Impact of Bias on Drift Detection in AI Health Software
Artificial Intelligence in MedicineJun 2023, Pages 313–322https://doi.org/10.1007/978-3-031-34344-5_37AbstractDespite the potential of AI in healthcare decision-making, there are also risks to the public for different reasons. Bias is one risk: any data unfairness present in the training set, such as the under-representation of certain minority groups, ...
- research-articleJune 2023
Preventing Discriminatory Decision-making in Evolving Data Streams
- Zichong Wang,
- Nripsuta Saxena,
- Tongjia Yu,
- Sneha Karki,
- Tyler Zetty,
- Israat Haque,
- Shan Zhou,
- Dukka Kc,
- Ian Stockwell,
- Xuyu Wang,
- Albert Bifet,
- Wenbin Zhang
FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and TransparencyJune 2023, Pages 149–159https://doi.org/10.1145/3593013.3593984Bias in machine learning has rightly received significant attention over the past decade. However, most fair machine learning (fair-ML) works to address bias in decision-making systems has focused solely on the offline setting. Despite the wide ...
- ArticleApril 2023
On the Change of Decision Boundary and Loss in Learning with Concept Drift
Advances in Intelligent Data Analysis XXIApr 2023, Pages 182–194https://doi.org/10.1007/978-3-031-30047-9_15AbstractConcept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity to evaluate model ...
- research-articleJanuary 2023
A Mechanism for Supporting the Peak/Trough Detection in the Concept Drifting Environment
Procedia Computer Science (PROCS), Volume 222, Issue C2023, Pages 262–271https://doi.org/10.1016/j.procs.2023.08.164AbstractThe primary objective of this research endeavor is to tackle the difficult challenge of identifying the elusive aspect peak/trough of the business cycle, which is known to exhibit the tendency of concept drift. To effectively confront this issue, ...
ENIDrift: A Fast and Adaptive Ensemble System for Network Intrusion Detection under Real-world Drift
ACSAC '22: Proceedings of the 38th Annual Computer Security Applications ConferenceDecember 2022, Pages 785–798https://doi.org/10.1145/3564625.3567992Machine Learning (ML) techniques have been widely applied for network intrusion detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer from fundamental limitations that hinder them from being deployed in the real ...