A paper referenced by Yu (2014) which introduced the FSVM to the domain.

The math model is mentioned by the Yu (2014) paper, The idea behind fuzzy SVM is to mitigate the over fitting problem in training the SVM. A weight (membership) is imposed so that the effect of outliers or noises are decreased. The fuzzy SVM is prosed as follows

\[\begin{align} && \min_{w,b,\xi}\ & \frac{1}{2}w^Tw + C\sum_k m_k\xi_k \\ \textrm{subject to}&& y_k(w^T \phi(x_k) + b) & \ge 1-\xi_k & \forall k \\ && \xi_k &\ge 0 & \forall k \end{align}\]The membership \(m_k\) of each training data shall be determined. The paper mentioned several approaches, such as distance measure between the data point and the class centre.

The novel idea of this paper is to use each training data point twice for binary classification problem, once for each classification result. In contrast to the previous fuzzy SVM, the author call this the bilateral-weighted fuzzy SVM (B-FSVM). For each of the \(N\) training data \((x_k, y_k)\), where \(y_k=\pm 1\), B-FSVM use it as \((x_k, 1, m_k)\) and \((x_k, -1, 1-m_k)\) so there will be \(2N\) training samples. In the tuple \((x,y,m)\), \(x\) is the input vector, \(y\) is the observed result, and \(m\in[0,1]\) is the membership. The classification problem is then

\[\begin{align} && \min_{w,b,\xi, \eta}\ & \frac{1}{2}w^Tw + C\sum_k [m_k\xi_k + (1-m_k)\eta_k] \\ \textrm{subject to}&& w^T \phi(x_k) + b & \ge 1-\xi_k & \forall k \\ && w^T \phi(x_k) + b & \le -1+\eta_k & \forall k \\ && \xi_k &\ge 0 & \forall k \\ && \eta_k &\ge 0 & \forall k \end{align}\]Note that there are two errors \(\xi\) and \(\eta\), which associate with weights \(m\) and \(1-m\) respectively. Formulate the above into Lagrangian function,

\[\begin{align} L(w,b,\xi,\eta;\alpha,\beta,\mu,\nu) = & \frac{1}{2}w^Tw + C\sum_k m_k\xi_k + C\sum_k (1-m_k)\eta_k \\ & - \sum_k \alpha_k [w^T \phi(x_k) + b - 1 + \xi_k ] \\ & + \sum_k \beta_k [w^T \phi(x_k) + b + 1 - \eta_k ] \\ & - \sum_k \mu_k\xi_k - \sum_k \nu_k\eta_k \end{align}\]and we have

\[\begin{align} \frac{\partial L}{\partial w} &= w - \sum_k\alpha_k\phi(x_k) + \sum_k\beta_k\phi(x_k) = 0 \\ \frac{\partial L}{\partial b} &= -\sum_k\alpha_k +\sum_k\beta_k = 0 \\ \frac{\partial L}{\partial \xi_k} &= Cm_k - \alpha_k - \mu_k = 0 & \forall k\\ \frac{\partial L}{\partial \eta_k} &= C(1-m_k) - \beta_k - \nu_k = 0 & \forall k\\ \alpha_k [w^T \phi(x_k) + b - 1 + \xi_k ] &=0 & \forall k \\ \beta_k [w^T \phi(x_k) + b + 1 - \eta_k ] &=0 & \forall k \\ \mu_k \xi_k &= 0 & \forall k \\ \nu_k \eta_k &= 0 & \forall k \end{align}\]where the last four above are from the KKT conditions. From these we have

\[\begin{gather} w = \sum_k (\alpha_k - \beta_k)\phi(x_k) \\ 0 \le \alpha_k \le Cm_k \\ 0 \le \beta_k \le C(1-m_k) \\ \xi_k(Cm_k-\alpha_k) = 0 \\ \eta_k[C(1-m_k)-\beta_k] = 0 \end{gather}\]and then

\[\begin{align} \sum_k \alpha_k [w^T \phi(x_k) + b - 1 + \xi_k ] - \sum_k \beta_k [w^T \phi(x_k) + b + 1 - \eta_k ] &=0 \\ \sum_k (\alpha_k - \beta_k) w^T \phi(x_k) + b \sum_k (\alpha_k - \beta_k) - \sum_k (\alpha_k + \beta_k) + \sum_k (\alpha_k \xi_k + \beta_k\eta_k) &= 0 \\ \sum_k (\alpha_k - \beta_k) w^T \phi(x_k) - \sum_k (\alpha_k + \beta_k) + \sum_k (\alpha_k \xi_k + \beta_k\eta_k) &= 0 \end{align}\]also,

\[\begin{align} \sum_k \xi_k(Cm_k - \alpha_k) + \sum_k \eta_k[C(1-m_k) - \beta_k] &= 0 \\ C \sum_k m_k \xi_k - \sum_k \alpha_k\xi_i + C \sum_k (1-m_k)\eta_k - \sum_k \beta_k\eta_k &= 0 \\ C \sum_k m_k \xi_k + C \sum_k (1-m_k)\eta_k &= \sum_k \alpha_k\xi_i + \sum_k \beta_k\eta_k \\ &= - \sum_k (\alpha_k - \beta_k) w^T \phi(x_k) + \sum_k (\alpha_k + \beta_k) \end{align}\]Substitute this back to the objective function of the original optimization problem:

\[\begin{align} & \frac{1}{2}w^Tw + C\sum_k [m_k\xi_k + (1-m_k)\eta_k] \\ =& \frac{1}{2}w^Tw - \sum_k (\alpha_k - \beta_k) w^T \phi(x_k) + \sum_k (\alpha_k + \beta_k) \\ =& \frac{1}{2}\sum_i\sum_j(\alpha_i-\beta_i)(\alpha_j-\beta_j)\phi(x_i)^T\phi(x_j) - \sum_i \sum_j (\alpha_i - \beta_i) (\alpha_j - \beta_j) \phi(x_i)^T\phi(x_j) + \sum_k (\alpha_k + \beta_k) \end{align}\]we can rewrite the problem as

\[\begin{align} \max_{\gamma, \beta}\ & -\frac{1}{2}\sum_i \sum_j \gamma_i\gamma_jK(x_i,x_j) + \sum_k \gamma_k + 2\sum_k\beta_k \\ \textrm{subject to } & \sum_k\gamma_k = 0 \\ & 0 \le \beta_k + \gamma_k \le Cm_k & \forall k \\ & 0 \le \beta_k \le C(1-m_k) & \forall k \end{align}\]where \(\gamma_k = \alpha_k - \beta_k\). Solved this for \(\alpha_k, \beta_k\), the final classifier is

\[y(x) = \mathrm{sign}\left(\sum_k (\alpha_k-\beta_k)K(x,x_k) + b\right)\]So far, the value of membership \(m_k\) is not determined. The paper summarized the procedure of training such fuzzy SVM as:

- Use basic credit scoring method to evaluate each training data for the credit score \(s_k\)
- Compute the membership \(m_k\) from \(s_k\)
- Solve the quadratic programming problem for the final classifier

which the membership can be computed according to linear scaling of all scores, or transformed the score by a logistic function, or transformed by a cumulative normal distribution function. The paper assumes we already have \(s_k\).

## Bibliographic data

```
@article{
author = "Yongqiao Wang and Shouyang Wang and K. K. Lai",
title = "A New Fuzzy Support Vector Machine to Evaluate Credit Risk",
journal = "IEEE Transactions on Fuzzy Systems",
volume = "13",
number = "6",
year = "2005",
pages = "820--831",
doi = "10.1109/TFUZZ.2005.859320",
}
```