Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (observations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this problem is by using ...

A major drawback associated with the use of classical statistical methods for business failure prediction on top of financial distress is their lack of high accuracy rate. This work analyses the use of the two-stage ensemble of multivariate discriminant analysis (MDA) and logit to improve predictive performance of classical statistical methods. All possible ratios are firstly built from the qua...

OBJECTIVE Primary aldosteronism (PA) is the most common endocrine cause of curable hypertension, but no single test unequivocally identifies it. Accordingly, we investigated the usefulness of a logistic multivariate discriminant analysis (MDA) approach for PA screening. DESIGN Generation of a logistic MDA function based on retrospective analysis of biochemical tests in a large cohort of refer...

In this work, we propose a new semi-supervised algorithm for remotely sensed hyperspectral image classification which belongs to the family of multiple classifier systems. The proposed approach combines the output of two well-established discriminative classifiers: sparse multinomial logistic regression (SMLR) and quadratic discriminant analysis (QDA). Our approach follows a two-step strategy. ...

The applications that are related to classification problem are wide-ranging. In fact, differentiating between patients with strong prospects for recovery and those highly at risk, between good credit risks and poor ones, or between promising new firms and those likely to fail, are among the most known of these applications. To solve such classification problem, several approaches have been app...

Logistic multiple regression using the method of maximum likelihood is now the method of choice for many regression-type problems involving binary, ordinal. or nominal dependent variables. Logistic regression does not require grouping of observations to obtain valid estimates of effects and of outcome probabilities, and it has been shown in the binary case to provide more accurate probability e...

Methods for non-destructive inspection of layered materials are becoming more and more popular as a way of assuring product integrity and quality. In this paper, we present a model-based technique using ultrasonic measurements for classification of thin bonding layers within three-layered materials. This could be, for example, an adhesive bond between two thin plates, where the integrity of the...

This study attempts to show how a Kohonen map can be used to improve the temporal stability of the accuracy of a financial failure model. Most models lose a significant part of their ability to generalize when data used for estimation and prediction purposes are collected over different time periods. As their lifespan is fairly short, it becomes a real problem if a model is still in use when re...