43,154 research outputs found
Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture
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ESRI vs BREWER: An Evaluation of Map Use with Alternative Colour Schemes amongst the General Public
This small study evaluates the effectiveness of selected sets of colour schemes used in ESRIâs ArcMap and ColorBrewer in communicating information on choropleth maps. Subjects conducted map reading tasks using online questionnaires and their performance was captured. The results did not show significant differences in performance associated with colour scheme - subjects were highly successful in direct acquisition tasks irrespective of the set(s) of scheme used. However, performances were consistently poor for âdistributionâ tasks. The results suggest limited spatial capabilities in the sample and highlight the need to test for general spatial ability in such experiments
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Dimensionality reduction and manifold learning methods such as t-Distributed
Stochastic Neighbor Embedding (t-SNE) are routinely used to map
high-dimensional data into a 2-dimensional space to visualize and explore the
data. However, two dimensions are typically insufficient to capture all
structure in the data, the salient structure is often already known, and it is
not obvious how to extract the remaining information in a similarly effective
manner. To fill this gap, we introduce \emph{conditional t-SNE} (ct-SNE), a
generalization of t-SNE that discounts prior information from the embedding in
the form of labels. To achieve this, we propose a conditioned version of the
t-SNE objective, obtaining a single, integrated, and elegant method. ct-SNE has
one extra parameter over t-SNE; we investigate its effects and show how to
efficiently optimize the objective. Factoring out prior knowledge allows
complementary structure to be captured in the embedding, providing new
insights. Qualitative and quantitative empirical results on synthetic and
(large) real data show ct-SNE is effective and achieves its goal
A Method for the Perceptual Optimization of Complex Visualizations
A common problem in visualization applications is the display of one surface overlying another. Unfortunately, it is extremely difficult to do this clearly and effectively. Stereoscopic viewing can help, but in order for us to be able to see both surfaces simultaneously, they must be textured, and the top surface must be made partially transparent. There is also abundant evidence that all textures are not equal in helping to reveal surface shape, but there are no general guidelines describing the best set of textures to be used in this way. What makes the problem difficult to perceptually optimize is that there are a great many variables involved. Both foreground and background textures must be specified in terms of their component colors, texture element shapes, distributions, and sizes. Also to be specified is the degree of transparency for the foreground texture components. Here we report on a novel approach to creating perceptually optimal solutions to complex visualization problems and we apply it to the overlapping surface problem as a test case. Our approach is a three-stage process. In the first stage we create a parameterized method for specifying a foreground and background pair of textures. In the second stage a genetic algorithm is applied to a population of texture pairs using subject judgments as a selection criterion. Over many trials effective texture pairs evolve. The third stage involves characterizing and generalizing the examples of effective textures. We detail this process and present some early results
Object Discovery via Cohesion Measurement
Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However, image pixels, which share a similar real-world color, may
be quite different since colors are often distorted by intensity. In this
paper, we reinvestigate the affinity matrices originally used in image
segmentation methods based on spectral clustering. A new affinity matrix, which
is robust to color distortions, is formulated for object discovery. Moreover, a
Cohesion Measurement (CM) for object regions is also derived based on the
formulated affinity matrix. Based on the new Cohesion Measurement, a novel
object discovery method is proposed to discover objects latent in an image by
utilizing the eigenvectors of the affinity matrix. Then we apply the proposed
method to both saliency detection and object proposal generation. Experimental
results on several evaluation benchmarks demonstrate that the proposed CM based
method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure
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