A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases - Ozcift, Akin; Gulten, Arif (Springer, 2012)
In biomedical studies, accuracy of classification algorithms used in disease diagnosis systems is certainly an important task and the accuracy of system is strictly related to extraction of discriminatory features from data. In this paper, we propose a new multi-class feature selection method based on Rotation Forest meta-learner algorithm. The feature selection performance of this newly proposed ensemble approach is tested on Erythemato-Squamous diseases dataset. The discrimination ability of selected features is evaluated by the use of several machine learning algorithms. In order to evaluate the performance of Rotation Forest Ensemble Feature Selection approach quantitatively, we also used various and widely utilized ensemble algorithms to compare effectiveness of resultant features. The new multi-class or ensemble feature selection algorithm exhibited promising results in eliminating redundant attributes. The Rotation Forest selection based features demonstrated accuracies between 98% and 99% in various classifiers and this is a quite high performance for Erythemato-Squamous Diseases diagnosis.
Keywords: Feature selection; Wrapper subset selection; Ensemble learning; Disease diagnosis; Boosting; Bagging; Rotation forest; Random forest; Classification performance; Erythemato-squamous diseases