Moderated VFDT in Stream Mining Using Adaptive Tie Threshold and Incremental Pruning

Yang, Hang; Fong, Simon

doi:10.1007/978-3-642-23544-3_36

Cited by 28 publications

(17 citation statements)

References 2 publications

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“…Practitioners benefit from this by comparing several algorithms in real-world scenarios and choosing the best fitting solution (Bifet et al 2010). Some VFDT-extended algorithms have been built on this platform such as Ensemble Hoeffding Tree, which is an online bagging method with some ensemble VFDT classifiers (Yang and Fong 2011). By adding a maximum number of split nodes, Adaptive Size Hoeffding Tree (ASHT) ) is derived from VFDT that is designed for handling concept-drift data streams.…”

Section: Related Workmentioning

confidence: 99%

MFlexDT: multi flexible fuzzy decision tree for data stream classification

2015

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In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic online fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables).

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Section: Related Workmentioning

confidence: 99%

MFlexDT: multi flexible fuzzy decision tree for data stream classification

2015

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“…Very Fast Decision Tree (VFDT) system is one of well-known pioneering decision tree algorithms designed for stream mining in software application; its lightweight design is capable of progressively building up a decision tree from scratch in real-time by accumulating sufficient statistics from data streams [1,2,7]. VFDT learns by incrementally updating the tree while scanning data.…”

Section: Introductionmentioning

confidence: 99%

Incrementally optimized decision tree for noisy big data

Yang¹,

Fong²

2012

Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Progra

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How to extract meaningful information from big data has been a popular open problem. Decision tree, which has a high degree of knowledge interpretation, has been favored in many real world applications. However noisy values commonly exist in high-speed data streams, e.g. real-time online data feeds that are prone to interference. When processing big data, it is hard to implement pre-processing and sampling in full batches. To solve this tradeoff, this paper proposes a new incremental decision tree algorithm so called incrementally optimized very fast decision tree (iOVFDT). The experiment evaluates the proposed algorithm in comparison to existing methods under noisy data streams environment. Result shows iOVFDT has outperformance on the aspects of higher accuracy and smaller model size.

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“…The Hoeffding Trees have sound guarantees of performance, a theoretically interesting feature not shared by other incremental decision tree learners. Figure 1 provides the Hoeffding Tree Induction Algorithm referenced from [6].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques look into fault detection and intrusion detection by exploring methods for identifying anomalies based on the scalability and generality of the data streams, they do not Figure 1. Algorithm 1 [6]: hoeffding tree induction algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

Muallem¹,

Shetty²,

Pan³

et al. 2017

JIS

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This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.

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Moderated VFDT in Stream Mining Using Adaptive Tie Threshold and Incremental Pruning

Cited by 28 publications

References 2 publications

MFlexDT: multi flexible fuzzy decision tree for data stream classification

MFlexDT: multi flexible fuzzy decision tree for data stream classification

Incrementally optimized decision tree for noisy big data

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

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