A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression

Abstract

How to handle concept drift problem is a big challenge for algorithms designed for the data streams. Currently, techniques related to the concept drift problem focus on single data stream. However, it normally needs to handle multiple relevant data streams in the real-world application. Current concept drift methods can not be directly used in the multistream setting. They can only be limitedly applied on each stream separately, which omits the drift correlation between streams. In the multi-stream scenario, when drift occurs in a stream, other streams may face or have faced a similar drift problem as well. This pattern of simultaneous or delayed occurrence of drift is critical to analyze and predict multiple streams as a whole dynamic system. To fill the gap in the multi-stream scenario, this paper proposes a fuzzy drift variance (FDV) to measure the correlated drift patterns among streams. FDA is able to present how the pattern of drift occurrence for any two streams correlates and how delayed this correlation is. Seven synthetic streams are designed to validate FDA. The experimental results show a good presentation ability of FDA for drift-correlated multiple streams.

From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors

“…Year Drift Type Sudden Gradual Incremental Recurring SSE-PBS [169] 2021 ODKK [161] 2021 RACE [160] 2021 LIR-eGB [184] 2021 CALMID [164] 2021 Nacre [133] 2021 SEDD [114] 2021 OFE-UECM [93] 2020 FDA [112] 2020 ACDDM [101] 2020 HDWM [153] 2020 OS-ELMs [123] 2020 DCS-LA [162] 2020 HLFR [110] 2019 RDWM [154] 2019 CSDD [132] 2019 ECPF [175] 2019 FHDDMS,FHDDMS add [100] 2018 FPDD, FSDD [130] 2018 FTRL-ADP [106] 2018 KME-TEST l [174] 2018 WSTD [128] 2018 MDDM [134] 2018 RDDM [96] 2017 ADDS [142] 2017 AL-ELM [172] 2017 FPH-DD [102] 2016 MOS-ELM [125] 2016 NDE [173] 2016 FHDDM [99] 2016 DOED [159] 2015 HDDM [97] 2015 ESOS-ELM [170] 2015 LFR [109] 2015 DDM-PHT [104] 2015 EDIST [140] 2014 OAUE [150] 2014 SEED [127] 2014 AGE [151] 2014 ACCD [165] 2014 ADDM [136] 2014 AUE2 [150] 2014 LEARN++.CDS [156] 2013 ECDD …”

“…Some other testing techniques were applied to monitor the model's performance degradation. Song et al [112] have proposed fuzzy error deviation (fed) metric, which is computed to estimate the drift severity based on the variation of the predictor error. Adaptive Online Incremental Learning for evolving data streams (AOIL) [113] monitors the change in the mean and variance values of the loss error to detect the drift.…”

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

“…Unlike traditional windowing methods, this approach employs sliding windows with an overlapping period to enable precise identification of the data instances that belong to different concepts. Focusing on multiple relevant data stream regression with concept drift, Song et al [224] developed a new adaptation model based on fuzzy drift variance, where the variance is designed to measure the correlated drift patterns among streams.…”

“…A couple of recent works [222], [224] in fuzzy data stream learning are focused on developing adaptive fuzzy models that can effectively handle concept drift in data streams. Learning from multiple stream [20] is a crucial and challenge problem in data stream learning, especially when streams have different rates, arrive asynchronously, or experience delays.…”