The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score accordin...

Linear discriminant analysis (LDA) is known to be inappropriate for the case of classes with unequal sample covariances. In recent years, there has been an interest in generalizing LDA to heteroscedastic discriminant analysis (HDA) by removing the equal within-class covariance constraint. This paper presents a new approach to HDA by defining an objective function which maximizes the class discr...

This paper presents a procedure for studying industrial performance and related issues such as changes in the wage structure. This procedure combines cluster analysis and discriminant analysis as a package, and applies this package to time series data. This enables us to organize industrial data into groups with similar wage or performance histories and then to extract summary time-series showi...

We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier system. The first GA version selects disjoint feature subsets to be used by the individual classifiers, whereas the second version selects (possibly) overlapping feature subsets, and also the types of the individual classifiers. The two GAs have been tested with four real data sets: Heart, Satimage, Lette...

Distance metric learning is widely used in many visual computing methods, especially image classification. Among various metric learning approaches, Fisher Discriminant Analysis (FDA) is a classical metric learning approach utilizing the pair-wise semantic similarity and dissimilarity in image classification. Moreover, Local Fisher Discriminant Analysis (LFDA) takes advantage of local data stru...

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method for classification inspired by biological mechanism. However, SFDA only considers the local geometrical structure information of data and ignores the global geometrical structure information. Furthermore, previous works have demonstrated that uncorrelated features of minimum redundancy are effective for classifi...

This article utilizes data envelopment analysis (DEA) to estimate the productivity of the English and Welsh police forces and to determine whether there are categorical scale effects in policing using multiple discriminant analysis (MDA). The article demonstrates that by using DEA efficiency results it is possible to make inferences about the optimal size and structure of the English and Welsh ...

Capsule Discriminant functions based on morphometric variables provide a reliable method for sex identification of free-living and hacked young Ospreys. Aims To describe an easy, accurate and low-cost method for sex determination of fully grown nestling and fledgling Ospreys Pandion haliaetus based on morphometric measurements. Methods Four different measurements were taken in 114 birds (40–73 ...

This paper proposes and uses multivariate methods as a tool to evaluate performances of the hardware of microcomputers using their performance data, speed and price. The evaluation is done by classifying the PCs into different categories in terms of their performances. In order to form these categories, the cluster analysis and discriminant analysis methods are used in sequence. The former grou...

Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture models (FMM) do not require, or even use if avai...