Fuzzy Support Vector Machine With Relative Density Information for Classifying Imbalanced Data

Yu, Hualong; Sun, Changyin; Yang, Xibei; Zheng, Shang; Zou, Haitao

doi:10.1109/tfuzz.2019.2898371

Cited by 76 publications

(30 citation statements)

References 42 publications

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“…Content may change prior to final publication. Yu et al presented a simple and effective method called different error costs (DEC), where the ratio between the misclassification cost for the minority instances and the majority instances should equal to the reciprocal of the quotient between their numbers of instances [21]. Therefore, according to the imbalance ratio (IR), we assign the reciprocal of the number of instances of the three types (majority, minority, and synthetic) as the corresponding misclassification costs.…”

Section: B the Framework Of Mklmomentioning

confidence: 99%

“…The best results corresponding to each dataset are highlighted in bold. Among them, the experimental results of SVM, ROS, SMOTE and RWO are taken from [21]. Similarly, we calculate the ranks of the algorithms for each real-world dataset, which are shown in Table 7.…”

Section: B Experiments With Artifical Datasetsmentioning

confidence: 99%

“…However, the relative contribution is estimated on the basis of Euclidean distance, for which the algorithm performs poorly on some sophisticated nonlinear data. Yu et al proposed two novel FSVMs for class imbalance learning (FSVM-CIL) based on the prior information, which calculate the relative density of each instance through the K-nearest neighbor probability density estimation (KNN-PDE) [21]. Fan et al used entropy to evaluate instances.…”

Section: Introductionmentioning

confidence: 99%

“…Note that in comparison to(8), the only change in the above dual function (21) is to extend the upper bound of the Lagrange multipliers and the post-balance Gram matrix. Due to the strong duality, J(d) in(18) is also the target value of the dual problem(21).…”

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confidence: 99%

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Multiple Kernel Learning With Minority Oversampling for Classifying Imbalanced Data

Wang

2021

IEEE Access

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Section: B the Framework Of Mklmomentioning

confidence: 99%

Section: B Experiments With Artifical Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multiple Kernel Learning With Minority Oversampling for Classifying Imbalanced Data

Wang

2021

IEEE Access

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“…Other Intelligent Hybrid Systems that have also been successful are: Fuzzy support vector machine (FSVM) giving support to class imbalance issues [20], Artificial immune system and genetic algorithm (AIS-GA) aims to automated diagnosis systems [21], Genetic algorithm, and particle swarm optimization (GA-PSO) used to gene selection [22], Deep learning and extreme learning machine (DELM) used in EEG classification [23].…”

Section: Introductionmentioning

confidence: 99%

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

et al. 2019

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Due to the rapid technological evolution and communications accessibility, data generated from different sources of information show an exponential growth behavior. That is, volume of data samples that need to be analyzed are getting larger, so the methods for its processing have to adapt to this condition, focusing mainly on ensuring the computation is efficient, especially when the analysis tools are based on computational intelligence techniques. As we know, if you do not have a good control of the handling of the volume of the data, some techniques that are based on learning iterative processes could represent an excessive load of computation and could take a prohibitive time in trying to find a solution that could not come close to desired. There are learning methods known as full batch, online and mini-batch, and they represent a good strategy to this problem since they are oriented to the processing of data according to the size or volume of available data samples that require analysis. In this first approach, synthetic datasets with a small and medium volume were used, since the main objective is to define its implementation and in experimentation phase through regression analysis obtain information that allows us to assess the performance and behavior of different learning methods under distinct conditions. To carry out this study, a Mamdani based neuro-fuzzy system with center-of-sets defuzzification with support of multiple inputs and outputs was designed and implemented that had the flexibility to use any of the three learning methods, which were implemented within the training process. Finally, results show that the learning method with best performances was Mini-Batch when compared to full batch and online learning methods. The results obtained by mini-batch learning method are as follows; mean correlation coefficient with 0.8268 and coefficient of determination with 0.7444, and is also the method with better control of the dispersion between the results obtained from the 30 experiments executed per each dataset processed.

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Kernel local outlier factor‐based fuzzy support vector machine for imbalanced classification

Wang

et al. 2021

Concurrency and Computation

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The problem of imbalanced data classification has become a research hotspot in the field of machine learning. Fuzzy support vector machine (FSVM) is an imbalanced classification processing method based on cost‐sensitive theory. The existing methods have cost‐sensitive, causing the prior distribution estimation of data inaccurate. This article proposes a novel FSVM algorithm based on the kernel local outlier factor (KLOF‐FSVM) for this problem. KLOF calculates the local outlier factor of the sample in the kernel space and assigns an appropriate membership value to the sample. This process enables the algorithm to obtain the distribution information of the data better. Compared with the algorithm based on distance only, KLOF has better robustness. It can expand the value range of majority class samples' membership degree to better balance the important relation between the minority class and the majority class. We selected some datasets in the Keel data repository and used cross‐validation to obtain the algorithm's effect under different evaluation indexes such as G‐Mean, F1 measure, and area under curve. By comparing with other algorithms, preliminary results show that this method has better classification quality.

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Fuzzy Support Vector Machine With Relative Density Information for Classifying Imbalanced Data

Cited by 76 publications

References 42 publications

Multiple Kernel Learning With Minority Oversampling for Classifying Imbalanced Data

Multiple Kernel Learning With Minority Oversampling for Classifying Imbalanced Data

An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

Kernel local outlier factor‐based fuzzy support vector machine for imbalanced classification

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