Breast cancer diagnosis using least square support vector machine

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.

A new fuzzy support vector machine to evaluate credit risk

The aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM). Also, we proposed a new cascade learning system based on Generalized Discriminant Analysis and Least Square Support Vector Machine. The proposed system consists of two stages. The first stage, we have used Generalized Discriminant Analysis to discriminant feature variables between healthy and patient (diabetes) data as pre-processing process. The second stage, we have used LS-SVM in order to classification of diabetes dataset. While LS-SVM obtained 78.21% classification accuracy using 10-fold cross validation, the proposed system called GDA-LS-SVM obtained 82.05% classification accuracy using 10-fold cross validation. The robustness of the proposed system is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 82.05% and it is very promising compared to the previously reported classification techniques.

A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine

Short-term wind power prediction based on LSSVM–GSA model

Analog circuits play a vital role in ensuring the availability of industrial systems. Unexpected circuit failures in such systems during field operation can have severe implications. To address this concern, we developed a method for detecting faulty circuit condition, isolating fault locations, and predicting the remaining useful performance of analog circuits. Through the successive refinement of the circuit's response to a sweep signal, features are extracted for fault diagnosis. The fault diagnostics problem is posed and solved as a pattern recognition problem using kernel methods. From the extracted features, a fault indicator (FI) is developed for failure prognosis. Furthermore, an empirical model is developed based on the degradation trend exhibited by the FI. A particle filtering approach is used for model adaptation and RUP estimation. This method is completely automated and has the merit of implementation simplicity. Case studies on two analog filter circuits demonstrating this method are presented.

Diagnostics and Prognostics Method for Analog Electronic Circuits

In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems.In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable regions for multiclass problems. We define a membership function in the direction perpendicular to the optimal separating hyperplane that separates a pair of classes. Using the minimum or average operation for these membership functions, we define a membership function for each class. Using some benchmark data sets, we show that recognition performance of fuzzy LS-SVMs with the minimum operator is comparable to that of fuzzy SVMs, but fuzzy LS-SVMs with the average operator showed inferior performance.

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Daisuke Tsujinishi

Papers

Fuzzy least squares support vector machines for multiclass problems