Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble

Qu, Wenyu; Zhang, Yang; Zhu, Jun; Qiu, Qiang

doi:10.1007/978-3-642-05224-8_24

Cited by 34 publications

(50 citation statements)

References 15 publications

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“…The first approach to MLC in data streams was a batch-incremental method that trains stacked BR classifiers (Qu et al 2009). Some methods for multi-class classification, such as Hoeffding Trees (HT) (Domingos and Hulten 2000), have also been adapted to the multi-label classification task (Read et al 2012).…”

Section: Multi-label Classification On Data Streamsmentioning

confidence: 99%

“…Most research into multi-label classification has been performed in the batch learning context. However, some effort has also been made to explore multi-label classification in the streaming setting (Qu et al 2009;Read et al 2012;), following the popularity of big data in the research community, as well as in industry. With an appropriate method, working in the streaming context allows for real-time analysis of large amounts of data, e.g., emails, blogs, RSS feeds, social networks, etc.…”

Section: Introductionmentioning

confidence: 99%

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Multi-label classification via multi-target regression on data streams

2016

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Multi-label classification (MLC) tasks are encountered more and more frequently in machine learning applications. While MLC methods exist for the classical batch setting, only a few methods are available for streaming setting. In this paper, we propose a new methodology for MLC via multi-target regression in a streaming setting. Moreover, we develop a streaming multi-target regressor iSOUP-Tree that uses this approach. We experimentally compare two variants of the iSOUP-Tree method (building regression and model trees), as well as ensembles of iSOUP-Trees with state-of-the-art tree and ensemble methods for MLC on data streams. We evaluate these methods on a variety of measures of predictive performance (appropriate for the MLC task). The ensembles of iSOUP-Trees perform significantly better on some of these measures, especially the ones based on label ranking, and are not significantly worse than the competitors on any of the remaining measures. We identify the thresholding problem for the task of MLC on data streams as a key issue that needs to be addressed in order to obtain even better results in terms of predictive performance.

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Section: Multi-label Classification On Data Streamsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-label classification via multi-target regression on data streams

2016

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“…These requirements can in fact be met by variety of learning schemes, including even batch learners (e.g., [1]), where batches are constantly gathered over time, and newer models replace older ones as memory fills up. Nevertheless, incremental methods remain strongly preferred in the data streams literature, and particularly the Hoeffding tree (HT) and its variations [2,3], k-nearest neighbors (kNN) [4].…”

Section: Introductionmentioning

confidence: 99%

Data Stream Classification Using Random Feature Functions and Novel Method Combinations

Read

Bifet

2015

2015 IEEE Trustcom/BigDataSE/Ispa

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Big Data streams are being generated in a faster, bigger, and more commonplace. In this scenario, Hoeffding Trees are an established method for classification. Several extensions exist, including high-performing ensemble setups such as online and leveraging bagging. Also, k-nearest neighbors is a popular choice, with most extensions dealing with the inherent performance limitations over a potentially-infinite stream.At the same time, gradient descent methods are becoming increasingly popular, owing in part to the successes of deep learning. Although deep neural networks can learn incrementally, they have so far proved too sensitive to hyperparameter options and initial conditions to be considered an effective 'off-theshelf' data-streams solution.In this work, we look at combinations of Hoeffding-trees, nearest neighbour, and gradient descent methods with a streaming preprocessing approach in the form of a random feature functions filter for additional predictive power.We further extend the investigation to implementing methods on GPUs, which we test on some large real-world datasets, and show the benefits of using GPUs for data-stream learning due to their high scalability.Our empirical evaluation yields positive results for the novel approaches that we experiment with, highlighting important issues, and shed light on promising future directions in approaches to data-stream classification.

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“…• 분류 [19,20,21,22] 스트림 데이터에 대한 실시간 의사 결정 지원 • 군집화 [23,24,25,26] 스트림 데이터의 유사성을 기반으로 실시간 그룹화 및 이상치 결정 지원 • 패턴 분석 [27,28,29] 스트림 데이터에 대한 빈발 패턴 및 이상치 결정 지원 질의 처리…”

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A Sensor Data Management System for USN based Fire Detection Application

Park¹,

Kim²

2011

Journal of the Korea Society of Computer and Information

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These days, the research of a sensor data management system for USN based real-time monitoring application is active thanks to the development and diffusion of sensor technology. The sensor data is rapidly changeable, continuous and massive row level data. However, end user is only interested in high level data. So, it is essential to effectively process the row level data which is changeable, continuous and massive. In this paper, we propose a sensor data management system with multi-analytical query function using OLAP and anomaly detection function using learning based classifier. In the experimental section, we show that our system is valid through

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Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble

Cited by 34 publications

References 15 publications

Multi-label classification via multi-target regression on data streams

Multi-label classification via multi-target regression on data streams

Data Stream Classification Using Random Feature Functions and Novel Method Combinations

A Sensor Data Management System for USN based Fire Detection Application

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