TY - GEN
T1 - A Classification Algorithm Based on Ensemble Feature Selections for Imbalanced-Class Dataset
AU - Yin, Hua
AU - Gai, Keke
AU - Wang, Zhijian
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/30
Y1 - 2016/6/30
N2 - Traditional classification algorithms addressing imbalanced-class dataset mostly concentrate on the majority classes' accuracy, such that the minority class's accuracy is usually ignored. Focusing on this issue, we propose a novel classification algorithm using Ensemble Feature Selections (EFS) for imbalanced-class dataset. This algorithm utilizes the superiority of EFS in accuracy, then considers the diversity and imbalance in the designing appropriate feature subset objective function to make it fit for the imbalanced dataset. It chooses the minority class-oriented F measurement for computing accuracy and imports a punishment-reward mechanism into the KW diversity measurement. When the minority class's accuracy goes up, the reward-factor is given. Otherwise, the punishment-factor is given. Comparing with four algorithms, our experimental evaluations have showed that Mostly our algorithm can improve the accuracy of minority class.
AB - Traditional classification algorithms addressing imbalanced-class dataset mostly concentrate on the majority classes' accuracy, such that the minority class's accuracy is usually ignored. Focusing on this issue, we propose a novel classification algorithm using Ensemble Feature Selections (EFS) for imbalanced-class dataset. This algorithm utilizes the superiority of EFS in accuracy, then considers the diversity and imbalance in the designing appropriate feature subset objective function to make it fit for the imbalanced dataset. It chooses the minority class-oriented F measurement for computing accuracy and imports a punishment-reward mechanism into the KW diversity measurement. When the minority class's accuracy goes up, the reward-factor is given. Otherwise, the punishment-factor is given. Comparing with four algorithms, our experimental evaluations have showed that Mostly our algorithm can improve the accuracy of minority class.
KW - Ensemble feature selections
KW - classification
KW - imbalance
KW - smart computing
UR - https://www.scopus.com/pages/publications/84979741908
U2 - 10.1109/BigDataSecurity-HPSC-IDS.2016.76
DO - 10.1109/BigDataSecurity-HPSC-IDS.2016.76
M3 - Conference contribution
AN - SCOPUS:84979741908
T3 - Proceedings - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
SP - 245
EP - 249
BT - Proceedings - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
A2 - Qiu, Meikang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Big Data Security on Cloud, IEEE BigDataSecurity 2016, 2nd IEEE International Conference on High Performance and Smart Computing, IEEE HPSC 2016 and IEEE International Conference on Intelligent Data and Security, IEEE IDS 2016
Y2 - 9 April 2016 through 10 April 2016
ER -