Stationary Mahalanobis kernel SVM for credit risk evaluation

Jiang, Hao; Ching, Wai‐Ki; Yiu, Ka Fai Cedric; Qiu, Yin Long

doi:10.1016/j.asoc.2018.07.005

Cited by 48 publications

(21 citation statements)

References 21 publications

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“…Then, only b must be found to obtain the prediction function f(x). According to the Karush-Kuhn-Tucker (KKT) conditions [18], several Equations are obtained as follows:…”

Section: Svrmentioning

confidence: 99%

Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Yan

Chen

et al. 2019

Water

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Water quality data cleaning is important for the management of water environments. A framework for water quality time series cleaning is proposed in this paper. Considering the nonlinear relationships among water quality indicators, support vector regression (SVR) is used to forecast water quality indicators when some indicators are missing or when they show abnormal values at a certain point in time. Considering the time series of water quality information, long short-term memory (LSTM) networks are used to forecast water quality indicators when all indicators are missing at a certain point in time. A parallel model based on particle swarm optimization (PSO) and LSTM is realized based on a microservices architecture to improve the efficiency of model execution and the predictive accuracy of the LSTM networks. The performance of the model is evaluated in terms of the mean absolute error (MAE) and root-mean-square error (RMSE). Inlet water quality data from a wastewater treatment plant in Gaobeidian, Beijing, China is considered as a case study to examine the effectiveness of this approach. The experimental results reveal that this model has better predictive accuracy than other data-driven models because of smaller MAE and RMSE and has an advantage in terms of time consumption compared with standalone serial algorithms.

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“…Then, only b must be found to obtain the prediction function f(x). According to the Karush-Kuhn-Tucker (KKT) conditions [18], several Equations are obtained as follows:…”

Section: Svrmentioning

confidence: 99%

Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Yan

Chen

et al. 2019

Water

View full text Add to dashboard Cite

show abstract

“…Despite major advancement towards credit scoring model development and their accuracy, the use of models in actual practice is still a challenge. This uptake is attributed to the number of computational methods currently developed, Neural Networks, Bayesian Networks and Genetic Algorithms operating in a black box manner making it difficult for users to have basic understanding of the system [5]. The approach makes the model developer at least the only one knowledgeable about the operation of the system which consequently shuns off third party users.…”

Section: Problem Statementmentioning

confidence: 99%

“…5 would have degree of membership of about 0.3 for Very Low i.e. VLOW membership function and membership of 0.7 for the Low membership function.…”

mentioning

confidence: 99%

Fuzzy Relational Model to Establish Credit Worthiness of Sacco Members in Kenya

Ahmed¹,

Njagi²,

Otieno³

2019

IJCA

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Credit scoring has provided a number of financial institutions like banks, Microfinance institutions the means of determining if a given client will default or repay their debt obligation. Credit defaulting has become a stubborn enemy to the financial sector globally. With numerous Saccos in Kenya today it is challenging to predict accurately the trust of its members hence there arise a need of models, which will determine Sacco members credit worthiness. Qualitative output variable (i.e. member credit worth) measured using factors (i.e. Credit Duration, Concurrent Credits, Repayment Amount, Most Valuable Asset and Account Balance with Sacco) are scaled using appropriate linguistic terms and fused using hierarchical sensory fusion to evaluate credit worth of Sacco members in Kenya. Similarly, the output variable member credit worthiness was assigned linguistic terms of Excellent, Good, Fair/ Average, Bad and Poor.

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“…Through a learning algorithm, it can automatically search for support vectors that have a superior discriminating ability to classify. A classifier constructed using SVM can maximise the distance between classes and has higher classification accuracy and better generalization; hence, it can obtain a superior learning and classification effect under the condition of small sample learning and can avoid the curse of dimensionality and over-learning problems (Guneralp et al, 2013;Jiang et al, 2018;Salimi et al, 2018). Application of SVM theory to hyperspectral remote sensing image classification can effectively overcome Hughes phenomenon and achieve superior results compared to conventional statistical pattern classification methods (Dalponte et al, 2008;Melgani & Bruzzone, 2004).…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm-based parameter optimization for EO-1 Hyperion remote sensing image classification

Zhang

Gui-cheng

2020

European Journal of Remote Sensing

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Because hyperspectral remote sensing images have high band dimension and limited training samples, it is hard to achieve acceptable classification results using conventional statistical pattern recognition methods. Nevertheless, support vector machine (SVM) is suitable for hyperspectral image classification owing to its good generalization ability and minimal structural risk. Aimed to address the problems of large subjectivity, excessive time consumption, and low optimization accuracy in traditional SVM parameter selection, we applied genetic algorithm to optimize the model parameters. Using EO-1 Hyperion data as an example, an SVM object classification algorithm was proposed based on genetic algorithm optimization. To determine the effectiveness and superiority of this algorithm, a comprehensive evaluation and comparative analysis were performed with the classification effects of the cross-validation and maximum likelihood methods, respectively. Experimental results demonstrated that the proposed algorithm can effectively avert the aimlessness of artificial parameter selection and automatically optimize the SVM model parameters. It achieved relatively high overall classification accuracy (91.23%), which was 16.42% and 4.48% higher than the maximum likelihood method and cross-validation method, respectively.

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Stationary Mahalanobis kernel SVM for credit risk evaluation

Cited by 48 publications

References 21 publications

Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Fuzzy Relational Model to Establish Credit Worthiness of Sacco Members in Kenya

Genetic algorithm-based parameter optimization for EO-1 Hyperion remote sensing image classification

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