A score-based preprocessing technique for class imbalance problems

Mirzaei, Behzad; Rahmati, Farshad; Nezamabadi‐pour, Hossein

doi:10.1007/s10044-022-01084-1

Cited by 9 publications

(3 citation statements)

References 76 publications

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“…The width parameter σ determines the flexibility of the classifier. When the value of σ decreases, the flexibility of the classifier for fitting the training data is increased, but it might lead to an over‐fitting problem 33 . Therefore, the parameters should be tuned to achieve the best detection performance.…”

Section: Resultsmentioning

confidence: 99%

Detection of paralytic shellfish toxins by near‐infrared spectroscopy based on a near‐Bayesian SVM classifier with unequal misclassification costs

Liu,

Xiong,

Qiao

et al. 2023

J Sci Food Agric

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BackgroundParalytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is imperative to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace.ResultsA rapid detection method for PST in mussels using near‐infrared spectroscopy (NIRS) technology has been proposed. The spectral data in the wavelength range of 950‐1700 nm for PST‐contaminated and non‐contaminated mussel samples have been used for building the detection model. To solve the imbalanced classification problem that the quantity of non‐contaminated mussels is far less than that of PST‐contaminated mussels in practical application, near‐bayesian support vector machines (NBSVM) with unequal misclassification costs (u‐NBSVM) have been applied. The u‐NBSVM model performs competitively on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u‐NBSVM does not show worse with the number of PST samples decreasing by adjusting the misclassification costs. When the number of PST samples is 20, the G‐mean and accuracy can reach 0.9898 and 0.9944, respectively.ConclusionCompared with the traditional SVM and the NBSVM, the u‐NBSVM model achieves better detection performance. The results of this study validate that the NIRS technology combined with the u‐NBSVM model can be used for rapid and non‐destructive PST detection in mussels.This article is protected by copyright. All rights reserved.

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Section: Resultsmentioning

confidence: 99%

Detection of paralytic shellfish toxins by near‐infrared spectroscopy based on a near‐Bayesian SVM classifier with unequal misclassification costs

Liu,

Xiong,

Qiao

et al. 2023

J Sci Food Agric

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“…A hybrid score-based model is proposed in [17] for handling class imbalance problems by integrating oversampling and under-sampling approaches. Based on the significance of the samples in the feature space, the authors use the sharing technique in both rounds (oversampling and under-sampling) to choose more appropriate samples.…”

Section: Related Workmentioning

confidence: 99%

SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

Kumar

2024

JCOMSS

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A significant research challenge in data mining and machine learning is class imbalance classification since the majority of real-world datasets are imbalanced. When the dataset is highly unbalanced, the majority of available classification techniques frequently underperform on minority-class cases. This is due to the fact that they disregard the relative distribution of each class in favor of maximizing the overall accuracy. Various techniques based on sampling methods, cost-sensitive learning, and ensemble methods have recently been employed to handle the class imbalance problem. This paper proposes a new clusteringbased under-sampling (US) technique, called SOM-US, for handling the class imbalance problem using the self-organized map (SOM). To validate the proposed approach, an experimental study was conducted to improve the capability of a classifierlogistic regression for software defect prediction by applying SOM-US over a NASA software defect dataset. The proposed approach was compared with six existing under-sampling methods on two performance measures. The results demonstrate that the SOM-US significantly improves the prediction capability of logistic regression over other under-sampling techniques for software defect prediction.

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“…A traditional criterion for categorizing objects into either being targets or not is the F1-score that is equivalent to Precision and Recall’s harmonic mean [ 30 , 35 , 66 ], i.e.,

…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Small Object Detection and Tracking: A Comprehensive Review

Mirzaei

Nezamabadi‐pour

Raoof

et al. 2023

Sensors

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Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability.

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A score-based preprocessing technique for class imbalance problems

Cited by 9 publications

References 76 publications

Detection of paralytic shellfish toxins by near‐infrared spectroscopy based on a near‐Bayesian SVM classifier with unequal misclassification costs

Detection of paralytic shellfish toxins by near‐infrared spectroscopy based on a near‐Bayesian SVM classifier with unequal misclassification costs

SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

Small Object Detection and Tracking: A Comprehensive Review

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