Visualization of Support Vector Machines with Unsupervised Learning

Hamel, Lutz

doi:10.1109/cibcb.2006.330984

Cited by 40 publications

(35 citation statements)

References 11 publications

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“…Only few attempts have been made to visualize decision boundaries for multi-dimensional data (Poulet 2008;Caragea et al 2001;Hamel 2006). We summarize those techniques and analyze which characteristics of the decision boundary they capture.…”

Section: Related Workmentioning

confidence: 99%

“…The method in (Hamel 2006) uses self-organizing maps (SOM) to visualize results of SVM. SOM's are also used to visualize decision boundaries in (Yan and Xu 2008).…”

Section: Related Workmentioning

confidence: 99%

“…Several attempts have been made to visualize decision boundaries for multi-dimensional data (Caragea et al 2001;Hamel 2006;Poulet 2008;Migut and Worring 2012). The first two methods are specific to limited types of classifiers and hence can not be used to compare different methods.…”

Section: Introductionmentioning

confidence: 99%

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Visualizing multi-dimensional decision boundaries in 2D

Migut

Worring

Veenman³

2013

Data Min Knowl Disc

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In many applications experts need to make decisions based on the analysis of multi-dimensional data. Various classification models can support the decision making process. To obtain an intuitive understanding of the classification model, interactive visualizations are essential. We argue that this is best done by a series of interactive 2D scatterplots. In this paper, we define a set of characteristics of the multi-dimensional classification model that have to be visually represented in those scatterplots. Our proposed method presents those characteristics in a uniform manner for both linear and non-linear classification methods. We combine a visualization of a Voronoi based representation of multi-dimensional decision boundaries with visualization of the distances of the data elements to these boundaries. To allow the developer of the model to refine the threshold of the classification model and instantly observe the results, we use interactive decision point selection on a performance curve. Finally, we show how the combination of those techniques allows exploration of multi-dimensional decision boundaries in 2D.

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Section: Related Workmentioning

confidence: 99%

“…The method in (Hamel 2006) uses self-organizing maps (SOM) to visualize results of SVM. SOM's are also used to visualize decision boundaries in (Yan and Xu 2008).…”

Section: Related Workmentioning

confidence: 99%

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Visualizing multi-dimensional decision boundaries in 2D

Migut

Worring

Veenman³

2013

Data Min Knowl Disc

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“…SVM can also be visualized using projections into lower dimensional subspaces [21,22], or using self-organizing maps from unsupervised learning [23]. These methods concentrate on visualization of separating hyperplane and decision surface.…”

Section: Related Workmentioning

confidence: 99%

Explaining Classifications For Individual Instances

Robnik–Šikonja

Kononenko

2008

IEEE Trans. Knowl. Data Eng.

222

120

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We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities. It is based on decomposition of a model's predictions on individual contributions of each attribute. Our method works for so called black box models such as support vector machines, neural networks, and nearest neighbor algorithms as well as for ensemble methods, such as boosting and random forests. We demonstrate that the generated explanations closely follow the learned models and present a visualization technique which shows the utility of our approach and enables the comparison of different prediction methods.

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“…However, in spite of the attempts to explain decisions of such techniques [4,5], classifiers based on these techniques are not transparent enough and are often considered as "black boxes". The transparency is very important in some application areas, such as medical decision support or quality control.…”

mentioning

confidence: 99%

A general framework for designing a fuzzy rule-based classifier

et al. 2010

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This is an accepted version of a paper published in Knowledge and Information Systems. This paper has been peer-reviewed but does not include the final publisher proofcorrections or journal pagination.Citation for the published paper: verikas, a., guzaitis, j., gelzinis, a., bacauskiene, m. Abstract This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, the classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy. By contrast, the classification accuracy of unseen data is increased due to the elimination.

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Visualization of Support Vector Machines with Unsupervised Learning

Cited by 40 publications

References 11 publications

Visualizing multi-dimensional decision boundaries in 2D

Visualizing multi-dimensional decision boundaries in 2D

Explaining Classifications For Individual Instances

A general framework for designing a fuzzy rule-based classifier

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