Proximal support vector machine techniques on medical prediction outcome
| Autor: | K. Drosou, Christos Koukouvinos |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology Computer science 02 engineering and technology computer.software_genre Imbalanced data Outcome (probability) Field (computer science) Support vector machine Statistical classification ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Binary classification 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Statistics Probability and Uncertainty Medical diagnosis computer |
| Zdroj: | Journal of Applied Statistics. 44:533-553 |
| ISSN: | 1360-0532 0266-4763 |
| Popis: | One of the major issues in medical field constitutes the correct diagnosis, including the limitation of human expertise in diagnosing the disease in a manual way. Nowadays, the use of machine learning classifiers, such as support vector machines (SVM), in medical diagnosis is increasing gradually. However, traditional classification algorithms can be limited in their performance when they are applied on highly imbalanced data sets, in which negative examples (i.e. negative to a disease) outnumber the positive examples (i.e. positive to a disease). SVM constitutes a significant improvement and its mathematical formulation allows the incorporation of different weights so as to deal with the problem of imbalanced data. In the present work an extensive study of four medical data sets is conducted using a variant of SVM, called proximal support vector machine (PSVM) proposed by Fung and Mangasarian [9]. Additionally, in order to deal with the imbalanced nature of the medical data sets we applied both a... |
| Databáze: | OpenAIRE |
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