Adaptive telecom churn prediction for concept-sensitive imbalance data streams
暂无分享,去创建一个
[1] Roberto Souto Maior de Barros,et al. RDDM: Reactive drift detection method , 2017, Expert Syst. Appl..
[2] Mehmed M. Kantardzic,et al. On the reliable detection of concept drift from streaming unlabeled data , 2017, Expert Syst. Appl..
[3] Zoran Bosni,et al. Detecting concept drift in data streams using model explanation , 2018 .
[4] Jie Lu,et al. Accumulating regional density dissimilarity for concept drift detection in data streams , 2018, Pattern Recognit..
[5] Xin Yao,et al. A Systematic Study of Online Class Imbalance Learning With Concept Drift , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[6] Russel Pears,et al. The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams , 2018, Expert Syst. Appl..
[7] Russel Pears,et al. Staged Online Learning: A new approach to classification in high speed data streams , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[8] Xin Yao,et al. Resampling-Based Ensemble Methods for Online Class Imbalance Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.
[9] Ishwar Baidari,et al. Bhattacharyya distance based concept drift detection method for evolving data stream , 2021, Expert Syst. Appl..
[10] Gregory Ditzler,et al. Incremental Learning of Concept Drift from Streaming Imbalanced Data , 2013, IEEE Transactions on Knowledge and Data Engineering.
[11] Adnan Amin,et al. Just-in-time customer churn prediction in the telecommunication sector , 2017, The Journal of Supercomputing.
[12] Marley M. B. R. Vellasco,et al. DetectA: abrupt concept drift detection in non-stationary environments , 2018, Appl. Soft Comput..
[13] Cheong Hee Park,et al. An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling , 2017, IEICE Trans. Inf. Syst..
[14] Wei Liu,et al. The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift , 2018, Neurocomputing.
[15] Edwin Lughofer,et al. Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances , 2016, Inf. Sci..
[16] Zhaoyun Ding,et al. A comprehensive active learning method for multiclass imbalanced data streams with concept drift , 2021, Knowl. Based Syst..
[17] Yun Sing Koh,et al. Detecting concept change in dynamic data streams , 2013, Machine Learning.
[18] R. Annie Uthra,et al. Comprehensive analysis for class imbalance data with concept drift using ensemble based classification , 2020, Journal of Ambient Intelligence and Humanized Computing.
[19] Theodoros Anagnostopoulos,et al. Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams , 2021, Smart Cities.
[20] Russel Pears,et al. Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[21] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[22] Gillian Dobbie,et al. Detecting Volatility Shift in Data Streams , 2014, 2014 IEEE International Conference on Data Mining.
[23] Koichiro Yamauchi,et al. Detecting Concept Drift Using Statistical Testing , 2007, Discovery Science.
[24] Cheong Hee Park,et al. Outlier and anomaly pattern detection on data streams , 2018, The Journal of Supercomputing.
[25] A. Bifet,et al. Early Drift Detection Method , 2005 .
[26] Nan Liu,et al. Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift , 2015, Neurocomputing.
[27] Yun Sing Koh,et al. One Pass Concept Change Detection for Data Streams , 2013, PAKDD.
[28] Guangjie Han,et al. Concept drift detection for data stream learning based on angle optimized global embedding and principal component analysis in sensor networks , 2017, Comput. Electr. Eng..
[29] Ana S. Camanho,et al. Predicting direct marketing response in banking: comparison of class imbalance methods , 2017 .