Imbalanced data classification based on scaling kernel-based support vector machine

Zhang, Yong; Fu, Panpan; Liu, Wenzhe; Chen, Guolong

doi:10.1007/s00521-014-1584-2

Cited by 61 publications

(21 citation statements)

References 32 publications

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“…Implementation of Kernel based Support Vector Machine (SVM) [26] technique is actually shown and described through Figure 2. This Kernel based SVM technique provides a lot of measures including processing of training dataset, selection of Kernel based on its capabilities and performance, dataset is divided in three sets i.e.…”

Section: Design and Development Of Proposed Forecasting Modelmentioning

confidence: 99%

A Supply Chain Management Based Patient Forecasting Model for Dental Hospital

2020

jcr

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Management is in fact on the verge of a major breakthrough in finding out exactly how service sector success is actually determined by the interactions between the flows of information, materials, money, manpower, and capital equipment. The way these five flow principles interlock to be able to amplify one another as well as to bring about switch and fluctuation will build the grounds for anticipating the look of decisions, policies, organizational forms, and investment choices . A concept of distribution management which recognized the integrated dynamics of organizational associations since companies are so intertwined, expert argued that technique attributes can influence the general functionality of abilities as investigation, engineering, sales, and promotion. Illustrated phenomena is actually utilizing a laptop computer or perhaps computer simulation of order information flow along with the impact of its effect on development in addition to distribution general functionality for each supply chain member, in addition to the entire supply chain application. A Machine Learning based proposed model for patient forecasting is implemented to show the research analysis work for patient forecasting for the dental hospital located in Bhopal, Madhya Pradesh, India. This research paper is indicating about the use, application and implementation of Kernel based Support Vector Machine for regression analysis purpose and also select the best Kernel for the implementation of the predicted model.

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Section: Design and Development Of Proposed Forecasting Modelmentioning

confidence: 99%

A Supply Chain Management Based Patient Forecasting Model for Dental Hospital

2020

jcr

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“…Along with the advent of advanced machine learning techniques, these techniques are also introduced into SVM to deal with imbalanced sample sets, including scaling kernel-based SVM [14], [15] and ensemble learning of SVM [16]. However, it is usually difficult to implement these techniques.…”

Section: Related Workmentioning

confidence: 99%

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Wang¹,

Yang²,

Ren³

2017

ETRI Journal

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Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

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“…A neuro-fuzzy modeling procedure was introduced in [21] to perform leave-one-out cross-validation on imbalanced datasets. A scaling kernel along-with the standard SVM was used in [22] to improve the generalization ability of learned classifiers for skewed datasets. Li et al [23] gave more importance to the minority class samples by setting weights with Adaboost during the training of an extreme learning machine (ELM).…”

Section: Introductionmentioning

confidence: 99%

“…These previous works hint towards the use of distinct costs for different training examples to improve the performance of the learning algorithm. However, they do not address the class imbalance learning of CNNs, which have recently emerged as the most popular tool for supervised classification, recognition and segmentation problems in computer vision [12,22,25,26,27]. Furthermore, they are mostly limited to the binary class problems [24,28], do not perform joint feature and classifier learning and do not explore computer vision tasks which inherently have imbalanced class distributions.…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

Khan

Hayat

Bennamoun

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

535

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Abstract-Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes is abundant making them an over-represented majority, and data of other classes is scarce, making them an underrepresented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority classes. In this work, we propose a cost-sensitive deep neural network which can automatically learn robust feature representations for both the majority and minority classes. During training, our learning procedure jointly optimizes the class-dependent costs and the neural network parameters. The proposed approach is applicable to both binary and multiclass problems without any modification. Moreover, as opposed to data level approaches, we do not alter the original data distribution which results in a lower computational cost during the training process. We report the results of our experiments on six major image classification datasets and show that the proposed approach significantly outperforms the baseline algorithms. Comparisons with popular data sampling techniques and cost-sensitive classifiers demonstrate the superior performance of our proposed method.

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Imbalanced data classification based on scaling kernel-based support vector machine

Cited by 61 publications

References 32 publications

A Supply Chain Management Based Patient Forecasting Model for Dental Hospital

A Supply Chain Management Based Patient Forecasting Model for Dental Hospital

Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

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