Credit Scoring Using Classification and Regression Tree (CART) Algorithm and Binary Particle Swarm Optimization

<span>Credit scoring is a procedure that exists in every financial institution. A way to predict whether the debtor was qualified to be given the loan or not and has been a major concern in the overall steps of the loan process. Almost all banks and other financial institutions have their own credit scoring methods. Nowadays, data mining approach has been accepted to be one of the well-known methods. Certainly, accuracy was also a major issue in this approach. This research proposed a hybrid method using CART algorithm and Binary Particle Swarm Optimization. Performance indicators that are used in this research are classification accuracy, error rate, sensitivity, specificity, and precision. Experimental results based on the public dataset showed that the proposed method accuracy is 78 %. In compare to several popular algorithms, such as neural network, logistic regression and support vector machine, the proposed method showed an outstanding performance. </span>

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Combining Binary Particle Swarm Optimization with Support Vector Machine for Enhancing Rice Varieties Classification Accuracy

IEEE Access ◽

10.1109/access.2021.3076130 ◽

2021 ◽

pp. 1-1

Author(s):

Tran Thi Kim Nga ◽

Tuan Pham-Viet ◽

Dang Minh Tam ◽

Insoo Koo ◽

Vladimir Y. Mariano ◽

...

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Classification Accuracy ◽

Particle Swarm ◽

Support Vector ◽

Binary Particle Swarm Optimization ◽

Rice Varieties ◽

Swarm Optimization

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Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2012-0009 ◽

2012 ◽

Vol 57 (5) ◽

Cited By ~ 5

Author(s):

Mohammad Reza Daliri

Keyword(s):

Feature Selection ◽

Particle Swarm Optimization ◽

Support Vector Machines ◽

Particle Swarm ◽

Support Vector ◽

Emission Computed Tomography ◽

Binary Particle Swarm Optimization ◽

Data Set ◽

Swarm Optimization ◽

Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

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

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026812500198 ◽

2012 ◽

Vol 11 (03) ◽

pp. 1250019 ◽

Cited By ~ 3

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.

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