Open AccessA novel αDistance Borderline-ADASYN-SMOTE algorithm for imbalanced data and its application in Alzheimer’s disease classification based on Dense Convolutional Network
Author(s) -
Feng Yan,
Jingjiao Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012046
Subject(s) - oversampling , computer science , k nearest neighbors algorithm , algorithm , data set , data mining , artificial intelligence , pattern recognition (psychology) , machine learning , computer network , bandwidth (computing)
The classification problem of imbalanced data has become a very important issue in the fields of machine learning and data mining. At present, relatively effective oversampling methods for processing imbalanced data include SMOTE, Borderline-SMOTE, and ADASYN. These algorithms have their own advantages; however, they do not adequately consider the distance factor, which is an important factor for balancing data precisely and reducing the misclassification probability of a minority boundary sample. Therefore, a new algorithm, αDistance Borderline-ADASYN-SMOTE algorithm, is proposed in the paper by combining the optimized Borderline-SMOTE algorithm with the optimized ADASYN algorithm. In the new algorithm, both the amount and the distance distribution of the nearest neighbor samples are considered. A few formulas are created to realize the algorithm. After being balanced by the algorithm, the data obtained from ADNI data set is trained, verified and tested by the Dense Convolutional Network. The experimental results show that the new model improves the classification performance of the Alzheimer’s disease.