scholarly journals Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shuang Pan ◽  
Jianguo Wei ◽  
Hao Pan
Keyword(s):  
Support Vector Machine ◽  
Evaluation Model ◽  
Risk Level ◽  
Support Vector ◽  
Empirical Parameter ◽  
Kernel Support Vector Machine ◽  
Svm Model ◽  
Online Lending ◽  
Kernel Svm ◽  
Hybrid Kernel

Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.

A scheduling procedure for a flow shop–like knowledgeable manufacturing cell with self-evolutionary features

2016 ◽  
Vol 230 (12) ◽  
pp. 2296-2306 ◽  
Author(s):  
Hong-Sen Yan ◽  
Wen-Chao Li
Keyword(s):  
Support Vector Machine ◽  
Learning Process ◽  
Flow Shop ◽  
Learning Ability ◽  
Support Vector ◽  
Manufacturing Cells ◽  
Kernel Support Vector Machine ◽  
Evolutionary Features ◽  
Knowledgeable Manufacturing ◽  
Hybrid Kernel

As a component of knowledgeable manufacturing systems, the structure of flow shop–like knowledgeable manufacturing cells is similar to that of a flow shop, thus representing an NP-hard issue. Here, we propose a self-evolutionary algorithm that exhibits learning ability and is composed of learning and scheduling modules. Unlike traditional scheduling algorithms, whose performances remain unchanged when the procedure is coded, the performance of the algorithm proposed in this study gradually improves as the learning process continues. The self-evolutionary ability is realized through the training of a hybrid kernel support vector machine. The hybrid kernel support vector machine was designed to approximate the value of the Q-function to select the appropriate action for the scheduling module and thus to obtain the optimal solution. An iterative process of value based on the Q-learning was adopted to train the hybrid kernel support vector machine to gradually enhance the algorithm’s efficiency and accuracy. The extracted state features of the flow shop–like knowledgeable manufacturing cells serve as inputs to hybrid kernel support vector machine for easy generalization of the learning results. The action exerted on a feasible solution is also defined as the input of the hybrid kernel support vector machine. The computational results show that the performance of the proposed procedure improves as the learning process progresses. Data from the computation and comparisons with other algorithms verify the validity and efficiency of the proposed algorithm.

Hybrid Kernel Support Vector Machine Classifier and Grey Wolf Optimization Algorithm Based Intelligent Classification Algorithm for Chronic Kidney Disease

2020 ◽  
Vol 10 (10) ◽  
pp. 2297-2307
Author(s):  
L. Jerlin Rubini ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Kidney Disease ◽  
Classification Model ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Novel Approach ◽  
Kernel Support Vector Machine ◽  
Hybrid Kernel

In the present day, distributed algorithms become more popular due to their diversity in several applications. The prediction and reorganization of medical data required more practice and information. We propose a novel approach feature selection based on efficient chronic kidney disease (CKD) prediction and classification. Primarily, the pre-processing pace will be implemented over the input data. Then, the grey wolf optimization (GWO) algorithm gets executed to choose the optimal features from the pre-processed data. Next, the projected technique exploits the Hybrid Kernel Support Vector Machine (HKSVM) as a classification model to identify the presence of CKD or not. The simulation takes place in MATLAB. The validation of the presented model takes place using a benchmark CKD dataset as of machine learning repository such as UCI under the presence of several measures. New outcome specified that the planned categorization arrangement has surpassed by containing enhanced 97.26% accuracy for kidney chronic dataset when contrasted with existing SVM technique only accomplished 94.77% and fuzzy min–max GSO neural network (FMMGNN) classifier accomplished 93.78%.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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