282,568 research outputs found
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Class imbalance classification is a challenging research problem in data
mining and machine learning, as most of the real-life datasets are often
imbalanced in nature. Existing learning algorithms maximise the classification
accuracy by correctly classifying the majority class, but misclassify the
minority class. However, the minority class instances are representing the
concept with greater interest than the majority class instances in real-life
applications. Recently, several techniques based on sampling methods
(under-sampling of the majority class and over-sampling the minority class),
cost-sensitive learning methods, and ensemble learning have been used in the
literature for classifying imbalanced datasets. In this paper, we introduce a
new clustering-based under-sampling approach with boosting (AdaBoost)
algorithm, called CUSBoost, for effective imbalanced classification. The
proposed algorithm provides an alternative to RUSBoost (random under-sampling
with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost)
algorithms. We evaluated the performance of CUSBoost algorithm with the
state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost,
SMOTEBoost on 13 imbalance binary and multi-class datasets with various
imbalance ratios. The experimental results show that the CUSBoost is a
promising and effective approach for dealing with highly imbalanced datasets.Comment: CSITSS-201
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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