Abstract:
This article introduces DCOT-LS-SVMs, a least-squares support vector machine-based deep cross-output knowledge transfer method. It improves the generalizability of least-squares support vector machines (LS-SVMs) without the complicated parameter tuning of kernel machines. The approach has two key features: 1) DCOT-LS-SVMs is based on a stacked hierarchical architecture with several layer-by-layer LS-SVM modules. The higher-layer module has additional input features that consider all previous module predictions and uses cross-output knowledge transfer to improve the learning process in the current module. This method simplifies learning by randomly assigning model parameters like tradeoff parameter C and kernel width to each module. DCOT-LS-SVMs uses a fast leave-one-out cross-validation strategy to autonomously determine the extent of cross-output knowledge transfer between adjacent modules. Since imbalanced datasets are common in real-world scenarios, we also present IDCOT-LS-SVMs. The proposed methods are compared to five comparative methods on UCI datasets and to a prostate cancer diagnosis case study.
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