10,625 research outputs found
Hellinger Distance Trees for Imbalanced Streams
Classifiers trained on data sets possessing an imbalanced class distribution
are known to exhibit poor generalisation performance. This is known as the
imbalanced learning problem. The problem becomes particularly acute when we
consider incremental classifiers operating on imbalanced data streams,
especially when the learning objective is rare class identification. As
accuracy may provide a misleading impression of performance on imbalanced data,
existing stream classifiers based on accuracy can suffer poor minority class
performance on imbalanced streams, with the result being low minority class
recall rates. In this paper we address this deficiency by proposing the use of
the Hellinger distance measure, as a very fast decision tree split criterion.
We demonstrate that by using Hellinger a statistically significant improvement
in recall rates on imbalanced data streams can be achieved, with an acceptable
increase in the false positive rate.Comment: 6 Pages, 2 figures, to be published in Proceedings 22nd International
Conference on Pattern Recognition (ICPR) 201
Deep Over-sampling Framework for Classifying Imbalanced Data
Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Learning from class-imbalanced data continues to be a common and challenging
problem in supervised learning as standard classification algorithms are
designed to handle balanced class distributions. While different strategies
exist to tackle this problem, methods which generate artificial data to achieve
a balanced class distribution are more versatile than modifications to the
classification algorithm. Such techniques, called oversamplers, modify the
training data, allowing any classifier to be used with class-imbalanced
datasets. Many algorithms have been proposed for this task, but most are
complex and tend to generate unnecessary noise. This work presents a simple and
effective oversampling method based on k-means clustering and SMOTE
oversampling, which avoids the generation of noise and effectively overcomes
imbalances between and within classes. Empirical results of extensive
experiments with 71 datasets show that training data oversampled with the
proposed method improves classification results. Moreover, k-means SMOTE
consistently outperforms other popular oversampling methods. An implementation
is made available in the python programming language.Comment: 19 pages, 8 figure
Box Drawings for Learning with Imbalanced Data
The vast majority of real world classification problems are imbalanced,
meaning there are far fewer data from the class of interest (the positive
class) than from other classes. We propose two machine learning algorithms to
handle highly imbalanced classification problems. The classifiers constructed
by both methods are created as unions of parallel axis rectangles around the
positive examples, and thus have the benefit of being interpretable. The first
algorithm uses mixed integer programming to optimize a weighted balance between
positive and negative class accuracies. Regularization is introduced to improve
generalization performance. The second method uses an approximation in order to
assist with scalability. Specifically, it follows a \textit{characterize then
discriminate} approach, where the positive class is characterized first by
boxes, and then each box boundary becomes a separate discriminative classifier.
This method has the computational advantages that it can be easily
parallelized, and considers only the relevant regions of feature space
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