Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.
In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.
Mahshid Khatibi
MK
Bardsiri
Mahdi
M
Eftekhari
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins