Journal ArticleDOI
A new fuzzy support vector machine to evaluate credit risk
TLDR
A new fuzzy support vector machine to discriminate good creditors from bad ones is proposed, reformulate this kind of two-group classification problem into a quadratic programming problem and expects it to have more generalization ability while preserving the merit of insensitive to outliers.Abstract:
Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen.read more
Citations
More filters
Journal ArticleDOI
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
Rukshan Batuwita,Vasile Palade +1 more
TL;DR: A method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise.
Journal ArticleDOI
Credit risk assessment with a multistage neural network ensemble learning approach
TL;DR: A multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level and the reliability values of the selected neural network models are scaled into a unit interval by logistic transformation.
Journal ArticleDOI
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches
TL;DR: This paper makes a full summary, analysis and evaluation on the current literatures of FDP from the following four unique aspects: definition of financial distress in the new century, FDP modeling, sampling approaches for FDP, and featuring approaches forFDP.
Journal ArticleDOI
A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises
TL;DR: A common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises.
Journal ArticleDOI
Machine Learning in Financial Crisis Prediction: A Survey
TL;DR: This paper presents the current achievements and limitations associated with the development of bankruptcy-prediction and credit-scoring models employing machine learning, and provides suggestions for future research.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Journal ArticleDOI
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Book
Least Squares Support Vector Machines
TL;DR: Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS- sVM for Unsupervised Learning LS- SVM for Recurrent Networks and Control.