计算机科学
人工智能
模式识别(心理学)
支持向量机
模糊逻辑
人工神经网络
分类器(UML)
融合
卷积(计算机科学)
特征(语言学)
机器学习
哲学
语言学
作者
Yang Cheng,Sung‐Kwun Oh,Bo Yang,Witold Pedrycz,Lin Wang
标识
DOI:10.1016/j.eswa.2022.117392
摘要
• The convolution-base and composite kernel are used for alleviating overfitting. • The composite kernel could adjust the nonlinear fitting ability of the classifier. • Hybrid fuzzy multi-(HFM) SVM leads to better results with flexible feature fusion. • The effectiveness of the HFM-SVM is demonstrated by three practical applications. Lately, Convolutional Neural Networks (CNNs) have been introduced to extract features and further enhance the classification performance in different application areas. In this study, a hybrid fuzzy multiple (HFM)-SVM design with the convolution-base (which consists of a series of pooling and convolutional layers) and composite kernel function is proposed. The objective of the proposed classifier is to enhance the nonlinear fitting ability of the classifier and improve the classification performance in high-dimensional applications. The key points of the proposed HFM SVM are enumerated as follows: 1) The convolution-base of the proposed classifier extracts features. The extracted features exhibit flexibility and applicability in high-dimensional applications. 2) The proposed HFM SVM designed with the composite kernel could adjust the nonlinear fitting ability for improving classification performance. The procedure of the proposed HFM SVM is described as follows: Convolution-base is considered as a preprocessing unit for extracting features. The features are not always linearly separable especially when being extracted from high dimensional data. A composite kernel function is constructed by considering the complicated (nonlinear) classification boundary into several local linear boundaries. The structure of the extracted features is captured by FCM clustering and integrated into the composite kernel function for enhancing the nonlinear fitting ability of the proposed classifier. The nonlinearity of the composite function can fill the gap between linear and nonlinear kernel functions by adjusting the number of clusters obtained by the FCM clustering algorithm. The proposed HFM SVM classifier based on composite kernel could improve the classification performance on high dimensional datasets. The performance of the proposed HFM SVM is experimented with and demonstrated by using three high-dimensional applications to show the effectiveness as well as performance improvement.
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