A novel concept drift detection method in data streams using ensemble classifiers

2016 ◽  
Vol 20 (6) ◽  
pp. 1329-1350 ◽  
Author(s):  
Mahdie Dehghan ◽  
Hamid Beigy ◽  
Poorya ZareMoodi
Keyword(s):  
Data Streams ◽  
Detection Method ◽  
Concept Drift ◽  
Ensemble Classifiers ◽  
Concept Drift Detection ◽  
Novel Concept

A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

2020 ◽  
Vol 31 (1) ◽  
pp. 309-320 ◽  
Author(s):  
Zhe Yang ◽  
Sameer Al-Dahidi ◽  
Piero Baraldi ◽  
Enrico Zio ◽  
Lorenzo Montelatici
Keyword(s):  
Incremental Learning ◽  
Detection Method ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Novel Concept

scholarly journals Minority Resampling Based Ensemble Framework Using Enhanced Early Drift Detection Method For Imbalanced Data Streams

2021 ◽  
Author(s):  
Priya S ◽  
Annie Uthra
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Detection Method ◽  
Concept Drift ◽  
Class Imbalance ◽  
Current Data ◽  
Classification Model ◽  
Ensemble Classifiers ◽  
K Nearest Neighbor ◽  
Jaccard Similarity

Abstract As the data mining applications are increasing popularly, large volumes of data streams are generated over the period of time. The main problem in data streams is that it exhibits a high degree of class imbalance and distribution of data changes over time. In this paper, Timely Drift Detection and Minority Resampling Technique (TDDMRT) based on K-nearest neighbor and Jaccard similarity is proposed to handle the class imbalance by finding the current ratio of class labels. The Enhanced Early Drift Detection Method (EEDDM) is proposed for detecting the concept drift and the Minority Resampling Method (KNN-JS) determines whether the current data stream should be regarded as imbalance and it resamples the minority instances in the drifting data stream. The K-Nearest Neighbors technique is used to resample the minority classes and the Jaccard similarity measure is established over the resampled data to generate the synthetic data similar to the original data and it is handled by ensemble classifiers. The proposed ensemble based classification model outperforms the existing over sampling and under sampling techniques with accuracy of 98.52%.

scholarly journals Real-Time Concept Drift Detection and Its Application to ECG Data

2021 ◽  
Vol 17 (10) ◽  
pp. 160
Author(s):  
Ketan Sanjay Desale ◽  
Swati Shinde
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Detection Method ◽  
Concept Drift ◽  
Model Performance ◽  
Adaptive Mechanism ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Concept Drift Detection ◽  
Electrocardiogram Ecg

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world & time-series datasets.

scholarly journals Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 349-371
Author(s):  
Hassan Mehmood ◽  
Panos Kostakos ◽  
Marta Cortes ◽  
Theodoros Anagnostopoulos ◽  
Susanna Pirttikangas ◽  
...  
Keyword(s):  
Real World ◽  
Data Streams ◽  
Smart City ◽  
Smart Cities ◽  
Concept Drift ◽  
Distributed Environment ◽  
Real World Data ◽  
Unique Challenge ◽  
World Data ◽  
Concept Drift Detection

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

On learning guarantees to unsupervised concept drift detection on data streams

2019 ◽  
Vol 117 ◽  
pp. 90-102 ◽  
Author(s):  
Rodrigo F. de Mello ◽  
Yule Vaz ◽  
Carlos H. Grossi ◽  
Albert Bifet
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Concept Drift Detection

scholarly journals Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2131 ◽  
Author(s):  
Affan Ahmed Toor ◽  
Muhammad Usman ◽  
Farah Younas ◽  
Alvis Cheuk M. Fong ◽  
Sajid Ali Khan ◽  
...  
Keyword(s):  
Data Streams ◽  
Detection Method ◽  
Concept Drift ◽  
Class Imbalance ◽  
Health Data ◽  
Smart Devices ◽  
Detection Delay ◽  
Medical Sensors ◽  
Synthetic Datasets ◽  
Almost All

With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments.

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