The majority of ensemble creation algorithms use the full set of available features for its task. Feature selection for ensemble creation has not been carried out except for some work on random feature selection. In this paper we focus our attention on genetic based feature selection for ensemble creation. Our approach uses a genetic algorithm to search over the entire feature space. Subsets of...

This paper investigates an ensemble feature selection algorithm that is based on genetic algorithms. The task of ensemble feature selection is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble’s classifiers...

The traditional motivation behind feature selection algorithms is to find the best subset of features for a task using one particular learning algorithm. Given the recent success of ensembles, however, we investigate the notion of ensemble feature selection in this paper. This task is harder than traditional feature selection in that one not only needs to find features germane to the learning t...

BACKGROUND AND OBJECTIVES The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such ski...

Ensemble methods are often used to decide on a good selection of features for later processing by a classifier. Examples of this are in the determination of Random Forest variable importance proposed by Breiman, and in the concept of feature selection ensembles, where the outputs of multiple feature selectors are combined to yield more robust results. All of these methods rely critically on the...

Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to...