The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Linear Programming Approaches for Multiple-Class Discriminant and Classification Analysis
Abstract
New linear programming approaches are proposed as nonparametric procedures for multiple-class discriminant and classification analysis. A new MSD model minimizing the sum of the classification errors is formulated to construct discriminant functions. This model has desirable properties because it is versatile and is immune to the pathologies of some of the earlier mathematical programming models for two-class classification. It is also purely systematic and algorithmic and no user ad hoc and trial judgment is required. Furthermore, it can be used as the basis to develop other models, such as a multiple-class support vector machine and a mixed integer programming model, for discrimination and classification. A MMD model minimizing the maximum of the classification errors, although with very limited use, is also studied. These models may also be considered as generalizations of mathematical programming formulations for two-class classification. By the same approach, other mathematical programming formulations for two-class classification can be easily generalized to multiple-class formulations. Results on standard as well as randomly generated test datasets show that the MSD model is very effective in generating powerful discriminant functions.
Related Content
|
Yu Bin, Xiao Zeyu, Dai Yinglong.
© 2024.
34 pages.
|
|
Liyin Wang, Yuting Cheng, Xueqing Fan, Anna Wang, Hansen Zhao.
© 2024.
21 pages.
|
|
Tao Zhang, Zaifa Xue, Zesheng Huo.
© 2024.
32 pages.
|
|
Dharmesh Dhabliya, Vivek Veeraiah, Sukhvinder Singh Dari, Jambi Ratna Raja Kumar, Ritika Dhabliya, Sabyasachi Pramanik, Ankur Gupta.
© 2024.
22 pages.
|
|
Yi Xu.
© 2024.
37 pages.
|
|
Chunmao Jiang.
© 2024.
22 pages.
|
|
Hatice Kübra Özensel, Burak Efe.
© 2024.
23 pages.
|
|
|