Evolving Stream Classification using Change Detection

Mustafa, Ahmad; Haque, Ahsanul; Khan, Latifur; Baron, Michael; Thuraisingham, Bhavani

doi:10.4108/icst.collaboratecom.2014.257769

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Almost all state-of-the-art stream classifiers divide the data into fixed chunk sizes, as h [Mustafa et al 2014]. There is a recent study for dynamic determination of chunk size according to concept drift speed [Mustafa et al 2014]. This problem is beyond the scope of our study.…”

Section: Goowe: Geometrically Optimum and Online-weighted Ensemblementioning

confidence: 96%

“…The data stream is sliced into chunks, each representing a single distribution. Almost all state-of-the-art stream classifiers divide the data into fixed chunk sizes, as h [Mustafa et al 2014]. There is a recent study for dynamic determination of chunk size according to concept drift speed [Mustafa et al 2014].…”

Section: Goowe: Geometrically Optimum and Online-weighted Ensemblementioning

confidence: 99%

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GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

Bonab,

Can

2017

Preprint

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Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the MOA libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with 8 state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.

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Section: Goowe: Geometrically Optimum and Online-weighted Ensemblementioning

confidence: 96%

Section: Goowe: Geometrically Optimum and Online-weighted Ensemblementioning

confidence: 99%

GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

Bonab,

Can

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The data stream is sliced into chunks, each representing a single distribution. Almost all state-ofthe-art stream classifiers divide the data into fixed chunk sizes, as h (Mustafa et al 2014). There is a recent study for dynamic determination of chunk size according to concept drift speed (Mustafa et al 2014).…”

Section: Goowe: Geometrically Optimum and Online-weighted Ensemblementioning

confidence: 99%

Goowe

Bonab

Can

2018

ACM Trans. Knowl. Discov. Data

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Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem, which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the Massive Online Analysis (MOA) libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with eight state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.

show abstract

“…Real-time anomaly detection [1][2][3][4][5][6] aims to capture abnormalities in system behavior in real time. These abnormalities or anomalies may appear in the form of malicious network intrusions [5,[7][8][9], malware infections, abnormal interaction patterns of individuals/groups in social media [10], overutilized system resources due to design defects, and so on.…”

Section: Introductionmentioning

confidence: 99%

Online anomaly detection for multi‐source VMware using a distributed streaming framework

et al. 2016

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Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non-conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data.This paper presents a novel, generic real-time distributed anomaly detection framework for multi-source stream data. As a case study, we investigate anomaly detection for a multi-source VMware-based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi-supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly.Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average.

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Evolving Stream Classification using Change Detection

Cited by 6 publications

References 29 publications

GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

Goowe

Online anomaly detection for multi‐source VMware using a distributed streaming framework

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