9,276 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
Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species
Invasive species are a major cause of ecological damage and commercial
losses. A current problem spreading in North America and Europe is the vinegar
fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and
healthy fruits and is therefore of concern to fruit growers, such as vintners.
Consequently, large amounts of data about infestations have been collected in
recent years. However, there is a lack of interactive methods to investigate
this data. We employ ensemble-based classification to predict areas susceptible
to infestation by D. suzukii and bring them into a spatio-temporal context
using maps and glyph-based visualizations. Following the information-seeking
mantra, we provide a visual analysis system Drosophigator for spatio-temporal
event prediction, enabling the investigation of the spread dynamics of invasive
species. We demonstrate the usefulness of this approach in two use cases
Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers
open access articleAutonomous robots that operate in the field can enhance their security and efficiency by
accurate terrain classification, which can be realized by means of robot-terrain interaction-generated
vibration signals. In this paper, we explore the vibration-based terrain classification (VTC),
in particular for a wheeled robot with shock absorbers. Because the vibration sensors are
usually mounted on the main body of the robot, the vibration signals are dampened significantly,
which results in the vibration signals collected on different terrains being more difficult to
discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade.
The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of
the existing feature-engineering and feature-learning classification methods; and (2) According to
the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM
(1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened
vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods,
which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project;
meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method
outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method
(LSTM) by 8.23%
- …