5,629 research outputs found
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
Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classification
With the abundance of industrial datasets, imbalanced classification has
become a common problem in several application domains. Oversampling is an
effective method to solve imbalanced classification. One of the main challenges
of the existing oversampling methods is to accurately label the new synthetic
samples. Inaccurate labels of the synthetic samples would distort the
distribution of the dataset and possibly worsen the classification performance.
This paper introduces the idea of weakly supervised learning to handle the
inaccurate labeling of synthetic samples caused by traditional oversampling
methods. Graph semi-supervised SMOTE is developed to improve the credibility of
the synthetic samples' labels. In addition, we propose cost-sensitive
neighborhood components analysis for high dimensional datasets and bootstrap
based ensemble framework for highly imbalanced datasets. The proposed method
has achieved good classification performance on 8 synthetic datasets and 3
real-world datasets, especially for high imbalance and high dimensionality
problems. The average performances and robustness are better than the benchmark
methods
A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
Imbalanced data classification is a major challenge in the field of data mining and machine learning, and oversampling algorithms are a widespread technique for re-sampling imbalanced data. To address the problems that existing oversampling methods tend to introduce noise points and generate overlapping instances, in this paper, we propose a novel oversampling method based on density peaks clustering. Firstly, density peaks clustering algorithm is used to cluster minority instances while screening outlier points. Secondly, sampling weights are assigned according to the size of clustered sub-clusters, and new instances are synthesized by interpolating between cluster cores and other instances of the same sub-cluster. Finally, comparative experiments are conducted on both the artificial data and KEEL datasets. The experiments validate the feasibility and effectiveness of the algorithm and improve the classification accuracy of the imbalanced data
Hybrid Preprocessing Method for Support Vector Machine for Classification of Imbalanced Cerebral Infarction Datasets
Cerebral infarction is one of the causes of ischemic stroke in the brain, and machine learning can be used in the detection of cerebral infarction in the brain. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. Support vector machine (SVM) is a machine learning method that is known for its high accuracy value. However, SVM can produce less optimal results if the data used is imbalanced. If imbalanced data is used, the resulting model will be biased. Therefore, this study uses a hybrid preprocessing method for SVM on the classification of an imbalanced cerebral infarction dataset obtained from the Department of Radiology at Dr. Cipto Mangunkusumo Hospital. This method is a combination of several sampling methods that deal with the problem of imbalanced data and utilizes undersampling and oversampling techniques in combination with SVM. Oversampling modifying the infarction dataset through the duplication of data with a small number of classes to be balanced with a large number of data classes. While undersampling reducing data with a large number of classes to be balanced with a smaller number of data classes. Undersampling and Oversampling are combined into a hybrid method. This method is a hybrid method of the undersampling and oversampling that can be used in SVM. The results of hybrid method using SVM will be compared with the undersampling and oversampling using SVM, individually. And SVM method without preprocessing the imbalanced dataset. The accuracy of the proposed method reached 94% in our evaluations for SVM using a hybrid preprocessing method
Oversampling for imbalanced learning based on k-means and smote
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsLearning 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
Minority Class Oversampling for Tabular Data with Deep Generative Models
In practice, machine learning experts are often confronted with imbalanced
data. Without accounting for the imbalance, common classifiers perform poorly
and standard evaluation metrics mislead the practitioners on the model's
performance. A common method to treat imbalanced datasets is under- and
oversampling. In this process, samples are either removed from the majority
class or synthetic samples are added to the minority class. In this paper, we
follow up on recent developments in deep learning. We take proposals of deep
generative models, including our own, and study the ability of these approaches
to provide realistic samples that improve performance on imbalanced
classification tasks via oversampling.
Across 160K+ experiments, we show that all of the new methods tend to perform
better than simple baseline methods such as SMOTE, but require different under-
and oversampling ratios to do so. Our experiments show that the way the method
of sampling does not affect quality, but runtime varies widely. We also observe
that the improvements in terms of performance metric, while shown to be
significant when ranking the methods, often are minor in absolute terms,
especially compared to the required effort. Furthermore, we notice that a large
part of the improvement is due to undersampling, not oversampling. We make our
code and testing framework available
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