Research on the incremental learning SVM algorithm based on the improved generalized KKT condition

Xie, Wenhao; Liang, Gongqian; Yuan, Philip F.

doi:10.1088/1742-6596/1237/2/022150

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…For the classification problem of imbalanced data, besides the improvement of the classical algorithms and the proposal of novel algorithms, some ensemble approaches have been proposed by integrating the classical algorithms with novel strategies, such as the three-way decision ensemble [21] and the samples' selection strategy [22]. In addition, the sampling algorithms for multi-class imbalanced data have been paid more and more attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

An oversampling method for multi-class imbalanced data based on composite weights

et al. 2021

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To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples.

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Section: Introductionmentioning

confidence: 99%

An oversampling method for multi-class imbalanced data based on composite weights

et al. 2021

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“…. Based on the abovementioned experiments, this round of experiment compares the training results of Simple-ISVM [22], KKT-ISVM [23,24], CSV-ISVM [14], GGKKT-ISVM [25], CD-ISVM [26], HDFC-ISVM, and HDFC-ISVM * (HDFC-ISVM * algorithm is the improved algorithm based on the original HDFC-ISVM algorithm) for the abovementioned 12 datasets in Table 12. In this experiment, for all the algorithms mentioned above, the initial training datasets contain 500 samples.…”

Section: Te Experimental Resultsmentioning

confidence: 99%

“…Previously, many classical ISVM learning algorithms have been proposed, including Simple_ISVM [22], KKT_ISVM [23,24], CSV_ISVM [14], GGKKT_ISVM [25], CD_ISVM [26], and other ISVM algorithms mentioned above, these algorithms provide the diferent selection methods of incremental learning training samples from diferent perspectives. However, the ability of the classifer to gradually accumulate the spatial distribution knowledge of samples is still not fully developed, so the accuracy and efciency can be further improved.…”

Section: Description Of the Original Hdfc-isvm Algorithmmentioning

confidence: 99%

An ISVM Algorithm Based on High-Dimensional Distance and Forgetting Characteristics

Xie

et al. 2022

Scientific Programming

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In the face of the batch, dynamic access data, or the flow of data that continuous changes over time, the traditional support vector machine algorithm cannot dynamically adjust the previous classification model. To overcome this shortcoming, the incremental support vector machine (ISVM) algorithm is proposed. However, many incremental support vector algorithms still have shortcomings such as low efficiency, memory limitation, and poor generalization. This paper puts forward the new ISVM algorithm, HDFC-ISVM ∗ algorithm, based on the high-dimensional distance and forgetting characteristics. This paper firstly proposes the original HDFC-ISVM algorithm that first learns the distribution characteristics of the samples according to the distance between the samples and the normative hyperplane. Then, it introduces the forgetting factor. In the incremental learning process, the classifier gradually accumulates the spatial distribution knowledge of samples, eliminates the samples that have no contributions to the classifier, and selectively forgets some useless samples according to the forgetting factor, which overcomes the shortcomings such as low efficiency and poor accuracy of some algorithms. But, the original HDFC-ISVM algorithm is sensitive to parameters, and different settings of the parameters have a great impact on the final classification accuracy of the algorithm. Therefore, on the basis of the original algorithm, an improved algorithm HDFC-ISVM ∗ based on the adjustments to the initialization strategy and updating rules of the forgetting factor is proposed. The initialization strategy and updating rules of the forgetting factor are adjusted to adapt datasets with different distributions in this improved algorithm. The rationality of the improved strategy about the forgetting factor is discussed theoretically. At the same time, the proposed algorithm has better classification accuracy, classification efficiency, and better generalization ability than other algorithms, which is verified by experiments.

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Incremental learning of upper limb action pattern recognition based on mechanomyography

Zhao

Cao²,

Zhang³

et al. 2023

Biomedical Signal Processing and Control

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Research on the incremental learning SVM algorithm based on the improved generalized KKT condition

Cited by 4 publications

References 3 publications

An oversampling method for multi-class imbalanced data based on composite weights

An oversampling method for multi-class imbalanced data based on composite weights

An ISVM Algorithm Based on High-Dimensional Distance and Forgetting Characteristics

Incremental learning of upper limb action pattern recognition based on mechanomyography

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