Application of the algorithm based on the PSO and improved SVDD for the personal credit rating

2014 ◽  
Vol 01 (04) ◽  
pp. 1450037 ◽  
Author(s):  
Sulin Pang ◽  
Shuqing Li ◽  
Jinwang Xiao
Keyword(s):  
Hybrid Method ◽  
Credit Rating ◽  
Credit Scoring ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Personal Credit ◽  
Credit Data

Considering the question of personal credit rating, this paper proposes a hybrid method for credit assessment based on an improved Support Vector Data Description (SVDD) algorithm combined with the particle swarm optimization (PSO) algorithm. First, the paper carries out data preprocess, and then it solves the two problems: parameters optimization and feature selection at the same time using the PSO algorithm combined with the improved SVDD algorithm and assesses the credit data using the optimized parameters and features. Finally, the method constructed is tested through two data sets in practice, and the results show that the hybrid method constructed in this paper can obtain higher classification accuracy compared with some other existing credit scoring methods.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

2012 ◽  
pp. 220-239
Author(s):  
Fei-Long Chen ◽  
Feng-Chia Li
Keyword(s):  
Nearest Neighbor ◽  
Credit Scoring ◽  
Back Propagation ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Hybrid Features ◽  
Credit Data ◽  
Learning Machine ◽  
Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

scholarly journals Towards Application of One-Class Classification Methods to Medical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Itziar Irigoien ◽  
Basilio Sierra ◽  
Concepción Arenas
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Biomedical Data ◽  
Vector Data ◽  
Target Class ◽  
Tumor Recognition ◽  
One Class Classification

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

scholarly journals An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

2018 ◽  
Vol 8 (12) ◽  
pp. 2574 ◽  
Author(s):  
Qinghua Mao ◽  
Hongwei Ma ◽  
Xuhui Zhang ◽  
Guangming Zhang
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Pso Algorithm ◽  
Conveyor Belt ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Steel Cord

Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of its parameters and the classification order. Therefore, an improved SDTSVM (ISDTSVM) algorithm is proposed in order to improve the classification accuracy of steel cord conveyor belt defects. In the proposed model, the classification order is determined by the sum of the Euclidean distances between multi-class sample centers and the parameters are optimized by the inertia weight Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the ISDTSVM algorithm with different feature space, experiments were conducted on multiple UCI (University of California Irvine) data sets and steel cord conveyor belt defects using the proposed ISDTSVM algorithm and the conventional SDTSVM algorithm respectively. The average classification accuracies of five-fold cross-validation were obtained, based on two kinds of kernel functions respectively. For the Vowel, Zoo, and Wine data sets of the UCI data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the SDTSVM algorithm. The classification accuracy of the radial basis function kernel were higher than the polynomial kernel. The results indicated that the proposed ISDTSVM algorithm improved the classification accuracy significantly, compared to the conventional SDTSVM algorithm.

Credit Scoring Using Support Vector Machine: A Comparative Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 6527-6533 ◽  
Author(s):  
S. Harikrishna ◽  
M.A.H. Farquad ◽  
Shabana
Keyword(s):  
Feature Selection ◽  
Credit Scoring ◽  
Decision Makers ◽  
Recursive Feature Elimination ◽  
Second Step ◽  
Support Vector ◽  
Ensemble Of Classifiers ◽  
Well Efficiency ◽  
Credit Data ◽  
Intelligent Models

Credit Scoring is the use of statistical/intelligent models to transform relevant data into numerical measures that guide the management and decision makers to make decisions such as accept/reject, pricing, pay/no pay and collections. This study focuses on predicting whether a credit applicant can be categorized as good or bad from the supplied data. Many researchers have recently worked on an ensemble of classifiers for such problems. It is observed from the literature that feature selection reduces the complexity of the system and improves the accuracy as well. Efficiency of SVM for feature selection and as a classifier in tandem and its application to credit scoring is analyzed in this paper. During the first step, SVM-RFE (Recursive Feature Elimination) is employed for feature selection and during the second step various architectures of SVM viz., Standard SVM, PSO-SVM and EVO-SVM are employed for classification purpose. The effectiveness of various approaches tested are evaluated using UK credit data and German credit data. It is observed that feature selection using SVM-RFE not only simplifies the process of credit scoring but also improves the accuracy of the system.

CREDIT SCORING USING MULTI-KERNEL SUPPORT VECTOR MACHINE AND CHAOS PARTICLE SWARM OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250019 ◽  
Author(s):  
YUN LING ◽  
QIUYAN CAO ◽  
HUA ZHANG
Keyword(s):  
Particle Swarm Optimization ◽  
Kernel Function ◽  
Credit Scoring ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Swarm Optimization

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

scholarly journals Reliable AI through SVDD and rule extraction

2021 ◽  
Author(s):  
Alberto Carlevaro
Keyword(s):  
Real Life ◽  
Statistical Error ◽  
Large Data ◽  
Parameter Tuning ◽  
Rule Extraction ◽  
Learning Problems ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Vehicle Platooning

<div><div><div><p>The proposed paper addresses how Support Vector Data Description (SVDD) can be used to detect safety regions with zero statistical error. It provides a detailed methodology for the applicability of SVDD in real-life applications, such as Vehicle Platooning, by addressing common machine learning problems such as parameter tuning and handling large data sets. Also, intelligible analytics for knowledge extraction with rules is presented: it is targeted to understand safety regions of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms.</p></div></div></div>

A pruned support vector data description-based outlier detection method: Applied to robust process monitoring

2020 ◽  
Vol 42 (11) ◽  
pp. 2113-2126 ◽  
Author(s):  
Ping Yuan ◽  
Zhizhong Mao ◽  
Biao Wang
Keyword(s):  
Process Monitoring ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Vector Data ◽  
Training Set ◽  
Data Set ◽  
Data Description ◽  
One Class Classifier ◽  
Comparative Results

Support vector data description (SVDD) is a boundary-based one-class classifier that has been widely used for process monitoring during recent years. However, in some applications where databases are often contaminated by outliers, the performance of SVDD would become deteriorated, leading to low detection rate. To this end, this paper proposes a pruned SVDD model in order to improve its robustness. In contrast to other robust SVDD models that are developed from the algorithmic level, we prune the basic SVDD from a data level. The rationale is to exclude outlier examples from the final training set as many as possible. Specifically, three different SVDD models are constructed successively with different training sets. The first model is used to extract target points by means of rejecting more suspect outlier examples. The second model is constructed using those extracted target points, and is used to recover some false outlier examples labeled by the first model. We build the third (final) model with the final training set consisting of target examples by the first model and false outlier examples by the second model. We validate our proposed method on 20 benchmark data sets and TE data set. Comparative results show that our pruned model could improve the robustness of SVDD more efficiently.

Sign in / Sign up

Export Citation Format

Share Document