Ensemble classifiers for biomedical data: Performance evaluation
Hanaa Ismail Elshazly, Abeer ElKorany, Aboul Ella Hassanien, Ahmad Taher Azar · 2013
Machine Learning concept offers the biomedical research field a great support. It provides many opportunities for disease discovering and related drugs revealing. The machine learning medical applications had been evolved from the physician needs and motivated by the promising results extracted from empirical studies. Medical support systems can be provided by screening, medical images, pattern classification and microarrays gene expression analysis. Typically medical data is characterized by its huge dimensionality and relatively limited examples. Feature selection is a crucial step to improve classification performance. Recent studies in machine learning field about classification process emerged a novel strong classifier scheme called the ensemble classifier. In this paper, a study for the performance of two novel ensemble classifiers namely Random Forest (RF) and Rotation Forest (ROT) for biomedical data sets is tested with five medical datasets. Three different feature selection methods were used to extract the most relevant features in each data set. Prediction performance is evaluated using accuracy measure. It was observed that ROT achieved the highest classification accuracy in most tested cases.