Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model
Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model
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DOI：
10.15807/jorsj.60.1
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发表时间：
2015-10
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影响因子：
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通讯作者：
Toshiki Sato;Yuichi Takano;Ryuhei Miyashiro
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文献类型：
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作者：
Toshiki Sato;Yuichi Takano;Ryuhei Miyashiro
本文讨论了序贯logit模型中特征子集的选择方法。Tanaka和Nakagawa（2014）提出了一种混合整数二次优化公式，用于解决基于逻辑损失函数的二次近似的问题。然而，由于逻辑损失函数和它的二次近似之间存在显著的差距，它们的公式可能无法找到一个好的特征子集。为了克服这个缺点，我们应用分段线性近似的逻辑损失函数。相应地，我们将最小化信息准则的特征子集选择问题框架化为混合整数线性优化问题。计算结果表明，我们的分段线性近似方法发现了一个更好的子集的功能比二次近似方法。
This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between the logistic loss function and its quadratic approximation, their formulation may fail to find a good subset of features. To overcome this drawback, we apply a piecewise-linear approximation to the logistic loss function. Accordingly, we frame the feature subset selection problem of minimizing an information criterion as a mixed integer linear optimization problem. The computational results demonstrate that our piecewise-linear approximation approach found a better subset of features than the quadratic approximation approach.