Membership Function-based Classification Algorithms for Stability improvements of BCI Systems
- Authors
- 염홍기; 심귀보
- Issue Date
- 2010
- Publisher
- 한국지능시스템학회
- Keywords
- Classification Algorithms; Optimal Hyperplanes; Membership Functions; Support Vector Machines; Variance Considered Machines.
- Citation
- International Journal of Fuzzy Logic and Intelligent systems, v.10, no.1, pp 59 - 64
- Pages
- 6
- Journal Title
- International Journal of Fuzzy Logic and Intelligent systems
- Volume
- 10
- Number
- 1
- Start Page
- 59
- End Page
- 64
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/33781
- ISSN
- 1598-2645
- Abstract
- To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate non-linear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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