Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD)

Alam, Shamshe; Sonbhadra, Sanjay Kumar; Agarwal, Sonali; Nagabhushan, P.; Tanveer, M.

doi:10.1016/j.patrec.2020.01.004

Cited by 20 publications

(12 citation statements)

References 31 publications

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“…1. Sample and decision boundary of a state-of-the-art boundary-point method [12] and of our method RAPID.…”

Section: Sample Boundarymentioning

confidence: 99%

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Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Englhardt¹,

Trittenbach²,

Kottke³

et al. 2020

Preprint

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Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy.In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.

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“…1. Sample and decision boundary of a state-of-the-art boundary-point method [12] and of our method RAPID.…”

Section: Sample Boundarymentioning

confidence: 99%

“…Even efficient solvers like decomposition methods [5][6][7][8] result in training times prohibitive for many applications. In these cases, sampling for data reduction is essential [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Englhardt¹,

Trittenbach²,

Kottke³

et al. 2020

Preprint

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show abstract

“…A new method was mentioned to select the most promising boundary data points as a training set. 20 Some data points should be ignored because they are the outliers that are less meaningful to the decision boundary. 21 Nevertheless, SVDD lacks this ability, which leads to poor performances on some data sets.…”

Section: Introductionmentioning

confidence: 99%

Boundary‐based Fuzzy‐SVDD for one‐class classification

Wang

et al. 2021

Int J of Intelligent Sys

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Support Vector Data Description (SVDD) is an extremely hot topic issue in One-Class Classification (OCC), which has displayed outstanding performance in dealing with many novelty detection problems. However, SVDD just takes the data description by the kernel-based distance among each instance into consideration rather than considering the distribution of the data. Therefore, Fuzzy Support Vector Data Description (Fuzzy-SVDD) has been developed to distribute a fuzzy membership to each input sample so that different samples cause different contributions to classification boundary. The majority of the methods in Fuzzy-SVDD are based on the sample density, but there are remaining two problems. These density-based Fuzzy-SVDD methods would decrease the contribution of support vectors (SVs) in low densities. What is more, these methods cannot get a precise density when there are few target samples. These two problems would lead to a poor classification boundary. To overcome these drawbacks, a novel method called Boundary-based Fuzzy-SVDD (BF-SVDD) is proposed in this paper. BF-SVDD uses a new definition called local-global center distance to search for the samples near the boundary. Then, it enhances fuzzy memberships of these samples because they carry more significant information for the decision boundary than other data.

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“…For OCC, many machine-learning algorithms have been proposed like: one-class random forest (OCRF), one-class deep neural network (OCDNN), one-class nearest neighbours (OCNN), one-class support vector classifiers (OCSVCs), etc. [1] , [2] , [7] , [19] , [29] , [35] . The benifit of OCSVCs over other state-of-the-art OCC techniques is its work-ability with only positive class samples whereas the other methods need negative class samples too for smooth operation, hence the OCSVCs are found more suitable for this research.…”

Section: Introductionmentioning

confidence: 99%

Target specific mining of COVID-19 scholarly articles using one-class approach

Sonbhadra

Agarwal

Nagabhushan

2020

Chaos, Solitons & Fractals

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Highlights In the presence of numerous research articles, extracting best-suited articles is timeconsuming and manually impractical. The objective of this paper is to extract the activity and trends of coronavirus related research articles using machine learning approaches to help the research community for future exploration concerning COVID-19 prevention and treatment techniques. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). These defined tasks describes the behavior of clusters to accomplish targetclass guided mining. Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

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Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD)

Cited by 20 publications

References 31 publications

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Boundary‐based Fuzzy‐SVDD for one‐class classification

Target specific mining of COVID-19 scholarly articles using one-class approach

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