Classification and disease prediction via mathematical programming

Lee, Eva K.; Wu, Tsung-Lin

doi:10.1063/1.2817343

Cited by 13 publications

(10 citation statements)

References 101 publications

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“…Since 1996, Lee and her medical colleagues have explored and demonstrated the capability of DAMIP in classifying various types of data arising from real biological and medical problems. DAMIP has been able to consistently maximize the correct classification rate (80%-100% correct rates were obtained) while satisfying pre-set limits on inter-group misclassifications (Gallagher et al 1996(Gallagher et al , 1997Feltus et al 2003Feltus et al , 2006Lee et al 2002Lee et al , 2004Lee 2007aLee , 2007bLee and Wu 2007). In these real applications, beyond reporting the tenfold cross-validation results, the resulting classification rule was also blind tested against new data of unknown group identity and resulted in remarkable rates of correct prediction.…”

Section: Estimating the Anderson Optimal Rule Via A Mixed Integer Promentioning

confidence: 88%

“…DAMIP consistently returned superior classification rates compared to other methods (80-100% correct classification rates) while satisfying pre-set limits on inter-group misclassifications (Gallagher et al 1996(Gallagher et al , 1997Feltus et al 2003Feltus et al , 2006Lee et al 2002Lee et al , 2004Lee 2007a, 2007b, Lee and Wu 2007. In these applications, the DAMIP model was solved using a specialized solver, MIPSOL, developed by Lee.…”

Section: Performance On Real-world Datamentioning

confidence: 98%

“…In addition to heart disease and thyroid disease classification, DAMIP has been applied successfully to prediction of ultrasonic cell disruption for drug delivery (Lee et al 2004), genomic analysis and prediction of aberrant CpG island methylation in human cancer (Feltus et al 2003(Feltus et al , 2006, and identification of tumor shape and volume in treatment of sarcoma (Lee et al 2002) applications. Further, Lee (2007aLee ( , 2007b, Lee and Wu (2007) demonstrated the classification capability of DAMIP over a wide variety of medical and biological problems in which DAMIP offers superior classification results (both in ten-fold cross-validation as well as in blind tests) when compared to other classification approaches.…”

Section: Introductionmentioning

confidence: 96%

“…Habbema et al (1974) introduce a twogroup decision-theoretic method. Lee and Wu provide a comprehensive summary of mathematical programming approaches for classification (Lee and Wu 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Commonly-used methods for classification include linear discriminant functions (LDF), decision trees (CART, C4.5), support vector machines (SVM) and other math programming approaches (Lee and Wu 2007), and artificial neural networks (ANN). These methods can be viewed as attempts at approximating a Bayes optimal rule for classification; that is, a rule which maximizes (minimizes) the total probability of correct classification (misclassification).…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model

Brooks

Lee

2008

Ann Oper Res

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Classification is concerned with the development of rules for the allocation of observations to groups, and is a fundamental problem in machine learning. Much of previous work on classification models investigates two-group discrimination. Multi-category classification is less-often considered due to the tendency of generalizations of two-group models to produce misclassification rates that are higher than desirable. Indeed, producing "good" two-group classification rules is a challenging task for some applications, and producing good multi-category rules is generally more difficult. Additionally, even when the "optimal" classification rule is known, inter-group misclassification rates may be higher than tolerable for a given classification model. We investigate properties of a mixed-integer programming based multi-category classification model that allows for the pre-specification of limits on inter-group misclassification rates. The mechanism by which the limits are satisfied is the use of a reserved judgment region, an artificial category into which observations are placed whose attributes do not sufficiently indicate membership to any particular group. The method is shown to be a consistent estimator of a classification rule with misclassification limits, and performance on simulated and real-world data is demonstrated.

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