Serum amylase and lipase measurements are often used to diagnose acute pancreatitis. This study addresses the question of whether it is advantageous to order serum amylase and lipase tests simultaneously. We evaluated performance of the two tests separately and in combination through a retrospective study of patients for whom both amylase and lipase determinations were ordered. Initial analysis...

A model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass datasets with more variables than observations. The variables selected by the proposed method ...

Artificial Intelligence (AI) -based rule induction techniques such as IXL and ID3 are powerful tools that can be used to classify firms as acquisition candidates or not, based on financial and other data. The purpose of this paper is to develop an expert system that employs uncertainty representation and predicts acquisition targets. We outline in this paper, the features of IXL, a machine lear...

Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of ...

Feed forward neural networks are compared with standard and new statistical classification procedures for the classification of proteins. We applied logistic regression, an additive model and projection pursuit regression from the methods based on a posterior probabilities; linear, quadratic and a flexible discriminant analysis from the methods based on class conditional probabilities, and the ...

In this paper we present a study that suggests a two-step approach, called the Iterative Refinement Algorithm (IRA), for improving the classification accuracy of inductive learning algorithms applied to very large datasets. We present the preliminary test results for IRA compared to other prediction methods including logistic regression, discriminant analysis, neural networks, C5, CART, and CHA...

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process and ...

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant ...

When analyzing business cycle data, one observes that the relevant predictor variables are often highly correlated. This paper presents a method to obtain measures of importance for the classification of data in which such multicollinearity is present. In systems with highly correlated variables it is interesting to know what changes are inflicted when a certain predictor is changed by one unit...

Support Vector Machines (SVMs) are known as some of the best learning models for pattern recognition, and an SVM can be used as a software reliability model to predict fault-prone modules from complexity metrics. We experimentally evaluated the prediction performance of an SVM model, comparing it with commonlyused conventional models including linear discriminant analysis, logistic regression, ...