3,557 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
An empirical evaluation of imbalanced data strategies from a practitioner's point of view
This research tested the following well known strategies to deal with binary
imbalanced data on 82 different real life data sets (sampled to imbalance rates
of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline
(just the base classifier). As base classifiers we used SVM with RBF kernel,
random forests, and gradient boosting machines and we measured the quality of
the resulting classifier using 6 different metrics (Area under the curve,
Accuracy, F-measure, G-mean, Matthew's correlation coefficient and Balanced
accuracy). The best strategy strongly depends on the metric used to measure the
quality of the classifier. For AUC and accuracy class weight and the baseline
perform better; for F-measure and MCC, SMOTE performs better; and for G-mean
and balanced accuracy, underbagging
Comparing the performance of oversampling techniques in combination with a clustering algorithm for imbalanced learning
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceImbalanced datasets in supervised learning are considered an ongoing challenging task for standard
algorithms, seeing as they are designed to handle balanced class distributions and perform poorly
when applied to problems of the imbalanced nature. Many methods have been developed to address
this specific problem but the more general approach to achieve a balanced class distribution is data
level modification, instead of algorithm modifications. Although class imbalances are responsible for
significant losses of performance in standard classifiers in many different types of problems, another
aspect that is important to consider is the small disjuncts problem. Therefore, it is important to
consider and understand solutions that not only take into the account the between-class imbalance
(the imbalance occurring between the two classes) but also the within-class imbalance (the imbalance
occurring between the sub-clusters of each class) and to oversample the dataset by rectifying these
two types of imbalances simultaneously. It has been shown that cluster-based oversampling is a robust
solution that takes into consideration these two problems. This work sets out to study the effect and
impact combining different existing oversampling methods with a clustering-based approach.
Empirical results of extensive experiments show that the combinations of different oversampling
techniques with the clustering algorithm k-means – K-Means Oversampling - improves upon
classification results resulting solely from the oversampling techniques with no prior clustering step
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
Komparasi Algoritma Kasifikasi dengan Pendekatan Level Data untuk Menangani Data Kelas Tidak Seimbang
Masalah data kelas tidak seimbang memiliki efek buruk pada ketepatan prediksi data. Untuk menangani masalah ini, telah banyak penelitian sebelumnya menggunakan algoritma klasifikasi menangani masalah data kelas tidak seimbang. Pada penelitian ini akan menyajikan teknik under-sampling dan over-sampling untuk menangani data kelas tidak seimbang. Teknik ini akan digunakan pada tingkat preprocessing untuk menyeimbangkan kondisi kelas pada data. Hasil eksperimen menunjukkan neural network (NN) lebih unggul dari decision tree (DT), linear regression (LR), naïve bayes (NB) dan support vector machine (SVM)
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