Uniform histograms for change detection in multivariate data

Boracchi, Giacomo; Cervellera, Cristiano; Macciò, Danilo

doi:10.1109/ijcnn.2017.7966060

Cited by 22 publications

(101 citation statements)

References 15 publications

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“…Moreover, identifying and including the data properties given in Table 1 into a method and model can pose challenges as well. The computational complexity in both the data set size n + m and dimensionality D, as well as the memory cost of a model at training time, but also at test time, can be a limiting factor (e.g., for data streams or in real-time monitoring [244]). Is the data-generating process assumed to be nonstationary [245]- [247] and are there distributional shifts expected at test time?…”

Section: Challenges In Anomaly Detectionmentioning

confidence: 99%

A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

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Section: Challenges In Anomaly Detectionmentioning

confidence: 99%

A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

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“…The main property of QuantTree is that its peculiar splitting scheme makes the distribution of any statistics T h independent on the data-generating distribution φ 0 . This properly is formally stated in the following theorem, that we proved in [4]. In practice, the distribution of any statistic T h depends only on the cardinalities of the training set and the window W and on the target probabilities {π k }.…”

Section: Quanttreementioning

confidence: 92%

“…We compare the histograms computed by QuantTree with other partitioning in the literature in [4] through experiments on Gaussian datastreams and real world datasets. Histograms computed by QuantTree yield a larger power and are the only ones that allows to properly control the false positive rate.…”

Section: Quanttreementioning

confidence: 99%

Learning and Adaptation to Detect Changes and Anomalies in High-Dimensional Data

Carrera

2019

Special Topics in Information Technology

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The problem of monitoring a datastream and detecting whether the data generating process changes from normal to novel and possibly anomalous conditions has relevant applications in many real scenarios, such as health monitoring and quality inspection of industrial processes. A general approach often adopted in the literature is to learn a model to describe normal data and detect as anomalous those data that do not conform to the learned model. However, several challenges have to be addressed to make this approach effective in real world scenarios, where acquired data are often characterized by high dimension and feature complex structures (such as signals and images). We address this problem from two perspectives corresponding to different modeling assumptions on the data-generating process. At first, we model data as realization of random vectors, as it is customary in the statistical literature. In this settings we focus on the change detection problem, where the goal is to detect whether the datastream permanently departs from normal conditions. We theoretically prove the intrinsic difficulty of this problem when the data dimension increases and propose a novel non-parametric and multivariate change-detection algorithm. In the second part, we focus on data having complex structure and we adopt dictionaries yielding sparse representations to model normal data. We propose novel algorithms to detect anomalies in such datastreams and to adapt the learned model when the process generating normal data changes.

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“…Giacomo et al [46] analysed two different approaches for building histograms in the context of change detection. When building histograms, nonparametric monitoring procedures were applied which implemented likelihood [47] [48] and distancebased approaches [49] [50].…”

Section: Several Drift Detection Methods Have Beenmentioning

confidence: 99%

A heterogeneous online learning ensemble for non-stationary environments

Idrees

Minku

Stahl

et al. 2020

Knowledge-Based Systems

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Learning in non-stationary environments is a challenging task which requires the updating of predictive models to deal with changes in the underlying probability distribution of the problem, i.e., dealing with concept drift. Most work in this area is concerned with updating the learning system so that it can quickly recover from concept drift, while little work has been dedicated to investigating what type of predictive model is most suitable at any given time. This paper aims to investigate the benefits of online model selection for predictive modelling in nonstationary environments. A novel heterogeneous ensemble approach is proposed to intelligently switch between different types of base models in an ensemble to increase the predictive performance of online learning in nonstationary environments. This approach is Heterogeneous Dynamic Weighted Majority (HDWM). It makes use of "seed" learners of different types to maintain ensemble diversity, overcoming problems of existing dynamic ensembles that may undergo loss of diversity due to the exclusion of base learners. The algorithm has been evaluated on artificial and real-world data streams against existing well-known approaches such as a heterogeneous Weighted Majority Algorithm (WMA) and a homogeneous Dynamic Weighted Majority (DWM). The results show that HDWM performed significantly better than WMA in non-stationary environments. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM.

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Uniform histograms for change detection in multivariate data

Cited by 22 publications

References 15 publications

A Unifying Review of Deep and Shallow Anomaly Detection

A Unifying Review of Deep and Shallow Anomaly Detection

Learning and Adaptation to Detect Changes and Anomalies in High-Dimensional Data

A heterogeneous online learning ensemble for non-stationary environments

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