Mst-bézier: an oversampling method for imbalanced data learning based on Bézier curves guided by minimum spanning trees

Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods

Biomedical data are widely accepted in developing prediction models for identifying a specific tumour, drug discovery and human cancers detection. However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets. This paper mainly focuses on reviewing and evaluating some popular and recently developed resampling and feature selection (FS) methods for class imbalance learning with data distribution being considered. Experimental results show that: 1) resampling and FS techniques exhibit better performance using support vector machine (SVM) classifier; 2) techniques such as random undersampling and FS perform better than other data pre-processing methods with T location-scale distribution when using SVM and K-nearest neighbours (KNN) classifiers. Random oversampling outperforms other methods on negative binomial distribution using Random Forest with lower level of imbalance ratio; 3) FS outperforms other data pre-processing methods in most cases, thus, FS with SVM classifier is the best choice for imbalanced biomedical data learning.

Learning from imbalanced datasets has recently received increasing research attention. Despite the remarkable results of AdaBoost in balanced situation, the imbalance problem remains to be solved. To address this, this paper proposes a novel method to improve the AdaBoost using a new error factor and a weighted voting parameter for a few classes and weak classifiers. Our method can weaken the dominant role of the majority class in iterative training by improving the classification ability of the minority class. The superiority of the proposed algorithm is finally proved theoretically and practically. Several real-world imbalance datasets in experiments as specific support, and our method outperforms previous algorithms, especially in the F-1 metric.

Imbalanced data are known to be notably difficult to deal with, as it needs a thorough understanding of the data to know how it should be done. However, it occurs in many fields, especially in finance, like banking. Many problems that use financial data can be summarized as binary problems. Some of them are fatal if not identified correctly. This research aims to find how to utilize machine learning models to deal with imbalanced data, specifically one that contains binary classification problems. In this paper, we use imbalanced insurance and credit card datasets. The research is conducted by starting from doing feature selection in the datasets by removing irrelevant columns, followed by using SMOTE algorithm variants (K-Means SMOTE and Borderline SMOTE) and pure SMOTE algorithm as the oversampling methods, and Near-Miss and All-KNN for undersampling methods. The algorithms are implemented by using scikit libraries. Lastly, PCA is used for dimensionality reduction and Logistic Regression as the machine learning model with cross-validations for deciding the best hyperparameter. The procedure produces five different Logistic Regression models that differ in how it handles the imbalances, which will be compared. The result shows that the oversampling methods work better than undersampling methods, with K-Means SMOTE and Borderline SMOTE performing better than the pure SMOTE, meaning that machine learning can be used as a solution to deal with binary classification problems in imbalanced financial data.

Learning from imbalanced data has conventionally been conducted on stationary data sets. Recently, there have been several methods proposed for mining imbalanced data streams, in which training data is read in consecutive data chunks. Each data chunk is considered as a conventional imbalanced data set, making it easy to apply sampling methods to balance data chunks. However, one drawback of chunk-based learning methods is that the update of classification models is delayed until a full data chunk is received. Therefore, this paper proposes a new method for online learning from imbalanced data streams, which uses naive Bayes as the base learner. To deal with the problem of class imbalance, a new training instance from the minority class is always involved in learning, but one from the majority class is only used with a small probability. In effect, this method corresponds to an under-sampling technique on imbalanced data streams. We show the effectiveness of the proposed online learning method on ten UCI data sets of various domains. Problems in the performance of naive Bayes on imbalanced data sets are also discussed.

Label correlation guided borderline oversampling for imbalanced multi-label data learning

This paper explores a simple and efficient baseline for multi-class and multiple objects tracking on VidOR dataset. The task is to build a robust object tracker that not only localize objects with bounding boxes in every video frame but also link the bounding boxes that indicate the same object entity into a trajectory. The task's challenges are the low resolution and imbalance of data and the disappearance of the object for a long time. According to the above characteristics, we design a robust detection model, proposed a new deep metric learning method, and explored some useful tracking algorithms to help complete the video object detection task.

Early breast cancer diagnosis is crucial for improving treatment outcomes for women. Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance. To tackle this issue, we propose a robust enhanced ensemble learning method (REEL). Specifically, a double-level over-sampling technology is developed to increase the diversity of synthesized minority breast cancer samples before model training, and an improved Random Forest is proposed to reconcile the bias and variance. In addition, a data-driven based particle swarm optimization algorithm automatically is used to select the value of parameters for base classifiers. Experimental results on breast cancer datasets and 19 other imbalanced datasets validate that our method outperforms other algorithms in terms of accuracy, F1 score, and AUC.These findings confirm that our method can further improve classification accuracy and has significant application value in the diagnosis of breast cancer.

Novel hybrid classification model for multi-class imbalanced lithology dataset

Fine-grained attribute analysis and data imbalance have always been research hotspots in the field of computer vision. Due to the complexity and diversity of fine-grained attribute images, traditional image classification methods have shortcomings in paying attention to fine-grained attributes of images and perform poorly when dealing with imbalanced data sets. To overcome these problems, this study proposes a fine-grained image threshold classification algorithm based on deep metric learning. By introducing a metric learning method, the focus on fine-grained attributes of images is enhanced. At the same time, by applying pairwise loss and proxy loss, the classification accuracy of the model is improved and the model convergence speed is accelerated. In order to deal with the problem of data imbalance, a classifier based on threshold analysis is designed. The classifier uses threshold analysis technology to achieve multi-level classification of fine-grained images, thereby improving the problem of low classification accuracy of a few categories in imbalanced data sets. Experimental results show that the proposed fine-grained image threshold classification algorithm based on deep metric learning is significantly superior to other methods in terms of classification accuracy.

Gaussian steering in continuousvariable (CV) systems, as a quantum correlation between nonlocality and entanglement, is an important quantum resource. Rapid detection of Gaussian steering is a significant challenge in quantum information process. In this paper, we employ a combination of machine learning methods, including Support Vector Machine (SVM), Backpropagation Neural Network (BPNN) and Meta-Weight-Net Neural Network (MWN) to speed up the detection. An ensemble learning approach that integrates these methods is also utilized to increase the accuracy of detection. A computable Gaussian steering quantification introduced recently in [Phys. Rev. A 110, 052427] serves as a pivotal tool for labeling the samples. A key observation is that steerable Gaussian states are vastly outnumbered by unsteerable ones, particularly in configurations where the untrusted party possesses significantly more modes than the trusted party. This leads to a highly skewed distribution of sample states in the dataset. In response to this phenomenon and to make comparison, we propose the imbalance factor and prepare three types of datasets to be trained: balanced datasets, naturally generated datasets and augmented datasets with via a data augmentation strategy. Numerical experiments for seven low modes scenarios reveal that the classifiers obtained by utilizing the ensemble learning method training on augmented dataset have the best overall performance, significantly improving generalization capabilities with low cost and high test accuracy, achieving detection times as fast as seconds, at least 100 times faster than calculating . The speed advantage of machine learning detection will be more obvious in the case of higher modes. Thus the approach is both efficient and reliable, offering valuable insights into the broader potential of machine learning applications in quantum information science and providing a robust framework for machine learning utilized to classification tasks, especially in data-imbalanced scenarios.

Australia’s critical water pipes break on average 7, 000 times per year. Being able to accurately identify which pipes are at risk of failure will potentially save Australia’s water utilities and the community up to \( \$700 \) million a year in reactive repairs and maintenance. However, ranking these water pipes according to their calculated risk has mixed results due to their different types of attributes, data incompleteness and data imbalance. This paper describes our experience in improving the performance of classifying and ranking these data via local metric learning. Distance metric learning is a powerful tool that can improve the performance of similarity based classifications. In general, global metric learning techniques do not consider local data distributions, and hence do not perform well on complex / heterogeneous data. Local metric learning methods address this problem but are usually expensive in runtime and memory. This paper proposes a fuzzy-based local metric learning approach that out-performs recently proposed local metric methods, while still being faster than popular global metric learning methods in most cases. Extensive experiments on Australia water pipe datasets demonstrate the effectiveness and performance of our proposed approach.

Extraction of insulators from power corridors is crucial for the construction of smart grids. Recent significant progress has been made in transmission line inspection through the application of Unmanned Aerial Vehicle Light Detection and Ranging (UAV LiDAR) systems, which can efficiently capture three-dimensional structural information. However, accurately extracting insulator components from massive point clouds remains challenging due to their high similarity in shape with surrounding pylons and power lines, as well as the inherent data imbalance issues. After a series of preprocessing steps, numerous multi-scale geometric features are designed and fused to precisely characterize insulators. Considering the issue of data imbalance due to the limited number of insulator samples, an Anomaly-Guided Progressively Balanced Ensemble (APE) Learning method is presented, using anomaly scores to achieve a good balance. Finally, the initial results are refined by incorporating spatial context information. The proposed delicate features and ensemble learning method were verified to be suitable for small insulator target extraction (including tangent insulators and tension insulators of different pylon types), achieving average precision, recall, and F1 scores of up to 95%. The results can promote intelligent inspection and renovation of large-scale power corridors.

Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers. This paper proposes a cost-sensitive ensemble method based on cost-sensitive support vector machine (SVM), and query-by-committee (QBC) to solve imbalanced data classification. The proposed method first divides the majority-class dataset into several subdatasets according to the proportion of imbalanced samples and trains subclassifiers using AdaBoost method. Then, the proposed method generates candidate training samples by QBC active learning method and uses cost-sensitive SVM to learn the training samples. By using 5 class-imbalanced datasets, experimental results show that the proposed method has higher area under ROC curve (AUC), F-measure, and G-mean than many existing class-imbalanced learning methods.