34,972 research outputs found
Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model
Combination of linear classifiers using score function -- analysis of possible combination strategies
In this work, we addressed the issue of combining linear classifiers using
their score functions. The value of the scoring function depends on the
distance from the decision boundary. Two score functions have been tested and
four different combination strategies were investigated. During the
experimental study, the proposed approach was applied to the heterogeneous
ensemble and it was compared to two reference methods -- majority voting and
model averaging respectively. The comparison was made in terms of seven
different quality criteria. The result shows that combination strategies based
on simple average, and trimmed average are the best combination strategies of
the geometrical combination
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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