4,224 research outputs found
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs
Shapelets are discriminative time series subsequences that allow generation
of interpretable classification models, which provide faster and generally
better classification than the nearest neighbor approach. However, the shapelet
discovery process requires the evaluation of all possible subsequences of all
time series in the training set, making it extremely computation intensive.
Consequently, shapelet discovery for large time series datasets quickly becomes
intractable. A number of improvements have been proposed to reduce the training
time. These techniques use approximation or discretization and often lead to
reduced classification accuracy compared to the exact method.
We are proposing the use of ensembles of shapelet-based classifiers obtained
using random sampling of the shapelet candidates. Using random sampling reduces
the number of evaluated candidates and consequently the required computational
cost, while the classification accuracy of the resulting models is also not
significantly different than that of the exact algorithm. The combination of
randomized classifiers rectifies the inaccuracies of individual models because
of the diversity of the solutions. Based on the experiments performed, it is
shown that the proposed approach of using an ensemble of inexpensive
classifiers provides better classification accuracy compared to the exact
method at a significantly lesser computational cost
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Multi-class protein fold classification using a new ensemble machine learning approach.
Protein structure classification represents an important process in understanding the associations
between sequence and structure as well as possible functional and evolutionary relationships.
Recent structural genomics initiatives and other high-throughput experiments have populated the
biological databases at a rapid pace. The amount of structural data has made traditional methods
such as manual inspection of the protein structure become impossible. Machine learning has been
widely applied to bioinformatics and has gained a lot of success in this research area. This work
proposes a novel ensemble machine learning method that improves the coverage of the classifiers
under the multi-class imbalanced sample sets by integrating knowledge induced from different base
classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have
compared our approach with PART and show that our method improves the sensitivity of the
classifier in protein fold classification. Furthermore, we have extended this method to learning over
multiple data types, preserving the independence of their corresponding data sources, and show
that our new approach performs at least as well as the traditional technique over a single joined
data source. These experimental results are encouraging, and can be applied to other bioinformatics
problems similarly characterised by multi-class imbalanced data sets held in multiple data
sources
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